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All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

On The Computational Capabilities of Physical Systems: Relationship With Conventional Computer Science - Part 2

In the first of this pair of papers, it was proven that there cannot be a physical computer to which one can properly pose any and all computational tasks concerning the physical universe. It was then further proven that no physical computer C can correctly carry out all computational tasks that can be posed to C. As a particular example, this result means that no physical computer that can, for any physical system external to that computer, take the specification of that external system's state as input and then correctly predict its future state before that future state actually occurs; one cannot build a physical computer that can be assured of correctly "processing information faster than the universe does". These results do not rely on systems that are infinite, and/or non-classical, and/or obey chaotic dynamics. They also hold even if one uses an infinitely fast, infinitely dense computer, with computational powers greater than that of a Turing Machine. This generality is a direct consequence of the fact that a novel definition of computation - "physical computation" - is needed to address the issues considered in these papers, which concern real physical computers. While this novel definition does not fit into the traditional Chomsky hierarchy, the mathematical structure and impossibility results associated with it have parallels in the mathematics of the Chomsky hierarchy. This second paper of the pair presents a preliminary exploration of some of this mathematical structure. Analogues of Chomskian results concerning universal Turing Machines and the Halting theorem are derived, as are results concerning the (im)possibility of certain kinds of error-correcting codes. In addition, an analogue of algorithmic information complexity, "prediction complexity", is elaborated. A task-independent bound is derived on how much the prediction complexity of a computational task can differ for two different reference universal physical computers used to solve that task, a bound similar to the "encoding" bound governing how much the algorithm information complexity of a Turing machine calculation can differ for two reference universal Turing machines. Finally, it is proven that either the Hamiltonian of our universe proscribes a certain type of computation, or prediction complexity is unique (unlike algorithmic information complexity), in that there is one and only version of it that can be applicable throughout our universe.

Wolpert, David H.↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2020

Physics successfully implements Lagrange multiplier optimization

Optimization is a major part of human effort. While being mathematical, optimization is also built into physics. For example, physics has the Principle of Least Action; the Principle of Minimum Power Dissipation, also called Minimum Entropy Generation; and the Variational Principle. Physics also has Physical Annealing, which, of course, preceded computational Simulated Annealing. Physics has the Adiabatic Principle, which, in its quantum form, is called Quantum Annealing. Thus, physical machines can solve the mathematical problem of optimization, including constraints. Binary constraints can be built into the physical optimization. In that case, the machines are digital in the same sense that a flip–flop is digital. A wide variety of machines have had recent success at optimizing the Ising magnetic energy. We demonstrate in this paper that almost all those machines perform optimization according to the Principle of Minimum Power Dissipation as put forth by Onsager. Further, we show that this optimization is in fact equivalent to Lagrange multiplier optimization for constrained problems. We find that the physical gain coefficients that drive those systems actually play the role of the corresponding Lagrange multipliers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

An Evaluation of The Dynamic Physical Security Risk Assessment Methodology for Fleet-Wide Applications

The requirements for U.S. nuclear power plants to maintain a large onsite physical security force contribute to their high operational costs. The cost of maintaining the current physical security posture is approximately 10% of the overall operation and maintenance budget for commercial nuclear power plants. The goal of the Light Water Reactor Sustainability (LWRS) program’s physical security pathway is to develop tools, methods, and technologies and provide the technical basis for an optimized physical security posture. The conservatisms built into current security postures may be analyzed and minimized to reduce security costs while still ensuring adequate security and operational safety. The research performed at Idaho National Laboratory within LWRS program’s physical security pathway has successfully developed a dynamic force-on-force modeling framework using various computer simulation tools and integrating them with the dynamic assessment Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool. This integrated process for physical security analysis is named Modeling and Analysis for Safety Security using Dynamic EMRALD Framework (MASS-DEF). This document provides an update on the progress in applying the MASS-DEF process to an operating commercial nuclear power plant as well as additional industry feedback regarding use of the tool for other physical security risk-informed topics. This report is only a summary of the progress and does not contain specific modeling results as those contain sensitive security information. Previous reports described how a user could integrate their plant-specific force-on-force models with the dynamic simulation tool EMRALD, model operator actions, and integrate with probabilistic risk assessment tools, such as CAFTA (Computer Aided Fault Tree Analysis System) or SAPHIRE (Systems Analysis Programs for Hands-on Integrated Reliability Evaluations), and with thermal-hydraulic tools, such as RELAP-5 or MAAP. Previous reports applied various combinations of available simulations codes with EMRALD using generic plant models to demonstrate how to perform the analysis. This report is an update the progress of applying the dynamic computational framework to an actual nuclear facility using their security scenarios and timelines. This report also provides an update to the procedural guidance for the MASS-DEF process and an overview of the generic models available for use by utilities. This report does not contain any plant’s sensitive information and/or safeguards information. This study’s purpose was to verify that the results achieved using generic models are similar to actual plant results and refine our guidance on the use of the framework. This assessment enables further analysis, such as what-if scenarios and staff-reduction evaluation, thereby optimizing physical security at plants.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Report on G4-Med, a Geant4 benchmarking system for medical physics applications developed by the Geant4 Medical Simulation Benchmarking Group

Geant4 is a Monte Carlo code extensively used in medical physics for a wide range of applications, such as dosimetry, micro- and nanodosimetry, imaging, radiation protection, and nuclear medicine. Geant4 is continuously evolving, so it is crucial to have a system that benchmarks this Monte Carlo code for medical physics against reference data and to perform regression testing. In this work, to respond to these needs, we developed G4-Med, a benchmarking and regression testing system of Geant4 for medical physics. G4-Med currently includes 18 tests. They range from the benchmarking of fundamental physics quantities to the testing of Monte Carlo simulation setups typical of medical physics applications. Both electromagnetic and hadronic physics processes and models within the prebuilt Geant4 physics lists are tested. The tests included in G4-Med are executed on the CERN computing infrastructure via the use of the geant-val web application, developed at CERN for Geant4 testing. The physical observables can be compared to reference data for benchmarking and to results of previous Geant4 versions for regression testing purposes. This paper describes the tests included in G4-Med and shows the results derived from the benchmarking of Geant4 10.5 against reference data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Scientific program for the Forward Physics Facility

The recent direct detection of neutrinos at the LHC has opened a new window on high-energy particle physics and highlighted the potential of forward physics for groundbreaking discoveries. In the last year, the physics case for forward physics has continued to grow, and there has been extensive work on defining the Forward Physics Facility and its experiments to realize this physics potential in a timely and cost-effective manner. Following a 2-page Executive Summary, we first present the status of the FPF, beginning with the FPF’s unique potential to shed light on dark matter, new particles, neutrino physics, QCD, and astroparticle physics. We then summarize the current designs for the Facility and its experiments, FASER2, FASER 2, FORMOSA, and FLArE.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2014

Energetic Particle Physics In Fusion Research In Preparation For Burning Plasma Experiments

The area of energetic particle (EP) physics of fusion research has been actively and extensively researched in recent decades. The progress achieved in advancing and understanding EP physics has been substantial since the last comprehensive review on this topic by W.W. Heidbrink and G.J. Sadler. That review coincided with the start of deuterium-tritium (DT) experiments on Tokamak Fusion Test reactor (TFTR) and full scale fusion alphas physics studies. Fusion research in recent years has been influenced by EP physics in many ways including the limitations imposed by the "sea" of Alfven eigenmodes (AE) in particular by the toroidicity-induced AEs (TAE) modes and reversed shear Alfven (RSAE). In present paper we attempt a broad review of EP physics progress in tokamaks and spherical tori since the first DT experiments on TFTR and JET (Joint European Torus) including helical/stellarator devices. Introductory discussions on basic ingredients of EP physics, i.e. particle orbits in STs, fundamental diagnostic techniques of EPs and instabilities, wave particle resonances and others are given to help understanding the advanced topics of EP physics. At the end we cover important and interesting physics issues toward the burning plasma experiments such as ITER (International Thermonuclear Experimental Reactor).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Research in Theoretical High Energy Physics- Final Report

PI Dr. Okada’s research interests are centered on phenomenological aspects of particle physics. It has been abundantly clear in recent years that an extension of the Standard Model (SM), i.e. new physics beyond the SM, is needed to explain a number of experimental observations such as the neutrino oscillation data, the existence of non-baryonic dark matter, and the observed baryon asymmetry of the Universe. In addition, the SM suffers from several theoretical/conceptual problems, such as the gauge hierarchy problem, the fermion mass hierarchy problem, and the origin of the electroweak symmetry breaking. It is believed that these problems can also be solved by new physics beyond the SM. The main purpose of the Dr. Okada’s research is a theoretical investigation of new physics opportunities from various phenomenological points of view, based on the recent progress of experiments/observations in particle physics and cosmology. There are many possibilities to go beyond the SM and many new physics models have been proposed. The major goal of the project is to understand the current status of possible new physics models and obtain the future prospects of new physics phenomena toward their discoveries.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Opportunities in Flavour Physics at the HL-LHC and HE-LHC

Motivated by the success of the flavour physics programme carried out over the last decade at the Large Hadron Collider (LHC), we characterize in detail the physics potential of its High-Luminosity and High-Energy upgrades in this domain of physics. We document the extraordinary breadth of the HL/HE-LHC programme enabled by a putative Upgrade II of the dedicated flavour physics experiment LHCb and the evolution of the established flavour physics role of the ATLAS and CMS general purpose experiments. We connect the dedicated flavour physics programme to studies of the top quark, Higgs boson, and direct high-$p_T$ searches for new particles and force carriers. We discuss the complementarity of their discovery potential for physics beyond the Standard Model, affirming the necessity to fully exploit the LHC's flavour physics potential throughout its upgrade eras.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

The Forward Physics Facility at the High-Luminosity LHC

High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF’s physics potential.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

6th Annual Midwest Conference for Undergraduate Women in Physics, January 18-20, 2013, Urbana, Illinois

This document is the program for the 6th Annual Midwest Conference for Undergraduate Women in Physics, which was held at the University of Illinois at Urbana-Champaign on January 18-20, 2013. The goals of the conference were to foster a culture in which undergraduate women are encouraged and supported to pursue, and also to succeed in, higher education in physics; to provide career information to students in physics and related fields; to give women the resources, motivation, and confidence to apply to graduate school and successfully complete a Ph.D. program in Physics; to provide information and dispel misconceptions about the application process for graduate school and the diverse employment opportunities in physics and related fields, enabling women to make more informed decisions about their goals and attain them; and to connect female physics students with successful female physicists to whom they can relate and who can act as inspirational role models and mentors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗
DOE OSTI2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗
DOE OSTI2014

MO-C-9A-01: Effective Medical Physics Educational Activities: Models and Methods

Medical physics is learned in a combination of activities including classroom sessions, individual study, small-group collaborative problem solving, and direct experience in the laboratory or clinical environment. Each type of learning activity is characterized by its effectiveness in producing the desired knowledge for the learner and the cost in terms of resources and human effort required providing it. While learning and teaching is a human activity, modern technology provides a variety of tools that can be used to enhance human performance. The class or conference room is the common setting for educational sessions in both academic institutions and continuing education conferences and programs such as those sponsored by the AAPM. A major value of a class/conference room program is efficiency by bringing a group of learners together to share in a common learning experience under the guidance of one or more experienced learning facilitators (lecturers or presenters). A major challenge is that the class/conference room is separated from the real world of medical physics. The design of an educational activity needs to take into consideration the desired outcomes with respect to what the learners should be able to do. The distinction is that of being able to apply the knowledge to perform specific physics functions rather than just knowing and being able to recall facts, and perhaps do well on written examinations. These are different types of knowledge structures within the human brain and distinctly different learning activities to develop each. Much of medical physics education, especially at the post-graduate and continuing education level, is for the purpose of enhancing the ability of physicists and other related professionals to perform applied procedures and tasks and requires specific types of knowledge.In this session we will analyze various learning activity models, the values and limitations of each, and how they can be used in medical physics education. An example we will use is optimizing CT image quality and dose which is an important topic for medical physicists, radiologists and residents, along with technologists. The knowledge structure for this is best developed by a combination of learning activities including class/conference discussions, individual study and review, and direct observation and interaction in the clinical setting under the direction of a knowledgeable leader.The function of the human brain will be considered with respect to learning experiences that contribute to effective medical physics knowledge structures. The characteristics of various types of educational activities will be compared with respect to their effectiveness for producing desired outcomes along with their limitations. Emphasis will be given to the design of highly-effective classroom/conference presentations, and activities will be demonstrated with an emphasis on using technology to enhance human performance of both learners and the learning facilitators. Learning Objectives: Develop and provide highly effective medical physics educational sessions. Use technology to enhance human performance in the educational process. Identify and analyze various models of educational activities Select and use educational activities that contribute value to the medical physics profession.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2014

MO-E-18C-06: Enriching Medical Physics Education By Visualizing The Invisible

Purpose: To enhance the understanding of medical physics concepts and develop higher levels of learning relating to invisible physics phenomena such as radiation. To provide medical physics educators in all countries of the world with understanding of knowledge structures in the human brain, the different levels of learning, and the types of knowledge required for higher level functions such as problem solving, creative innovations, and applied clinical applications. To provide medical physics educators with an open access resource (tool) that they can use in their teaching activities to enrich and elevate the level of learning for their students, residents, etc. with respect to the invisible realm of medical physics. Methods: An experienced clinical medical physicist and educator has created and provided with open access three complementary web-based resources to achieve the purposes described above. One is a module focusing on the medical physics learning process with respect to mental knowledge structures, how they relate to outcomes and applications, and learning activities that are required to develop the required knowledge structures. The second is an extensive set of visuals that educators can use in their activities (classes, small group discussions, etc.) to visualize the invisible. The third is an interactive online simulation where learners can adjust factors and visually observe changes in x-radiation.These resources are available online at www.BLINDED FOR REVIEW . Results: Medical physics education, especially for non-physicists, is becoming much more interesting and useful especially with respect to invisible radiation. The global impact is that medical imaging professionals can be more effective in optimizing x-ray imaging procedures and risk management when they have knowledge levels that enhance problem solving, innovation, and creativity. Conclusion: Medical physics educators in all institutions can be much more effective and efficient in the sharing of their knowledge and experience when enhanced with high-quality visuals.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2014

WE-E-19A-01: Globalization of Medical Physics

Following successful 2012–2013 International Professional Symposiums as a part of Annual AAPM meetings, representatives of AAPM and International Organization of Medical Physics (IOMP) suggested to make this tradiational Symposium a permanent part of Annual AAPM meetings in future. Following the tradition, this session includes presentations of representatives of AAPM, IOMP, European Federation of Medical Physics (EFOMP), International Atomic Energy Agency (IAEA) and International Center for Theoretical Physics (ICTP). The speakers will cover various aspects of International collaboration such as educational, professional, and scientific issues, as well as help to developing countries. With further developments of medicine and technology and increased communication with our colleagues overseas, Medical Physics becomes more and more global profession. Use of the same technology, significant progress in medical physics research and developing practical regulations worldwide makes it increasingly useful to organize global collaboration of medical physicists. Several international organizations are tasked to promote such collaboration and provide help to developing countries. Not all AAPM members are fully aware of these international efforts. It is very useful for medical physicists to know about success of our profession in other countries. Different schools present different approaches to the same problem, which allows to find the best solution. By communicating with colleagues overseas, one can learn more than from just reading scientific publications. At this session the attendees will receive a glimpse of International Medical Physics activities. Learning Objectives: Understand the globalization of Medical Physics profession and advantages of collaboration with foreign colleagues. See what role AAPM is playing in establishing contacts with colleagues overseas. Understand the role of IOMP and main directions of its activity. Learn about IAEA and how it helps developing countries. Learn about activity of EFOMP and how can help the global development of Medical Physics. Find out about ICTP and its educational programs.

07 ISOTOPE AND RADIATION SOURCES↗