Uncertainty Quantification and Machine Learning Algorithms for Physical Models: Tackling Computational Expense and High-Dimensionality.
Abstract not provided.
Safta, Cosmin↗
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Results for “computational physics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Fundamental Physics of Reversible Computing--An Introduction.
Abstract not provided.
Frank, Michael P↗
Analyzing high energy physics data using database computing: Preliminary report
A proof of concept system is described for analyzing high energy physics (HEP) data using data base computing. The system is designed to scale up to the size required for HEP experiments at the Superconducting SuperCollider (SSC) lab. These experiments will require collecting and analyzing approximately 10 to 100 million 'events' per year during proton colliding beam collisions. Each 'event' consists of a set of vectors with a total length of approx. one megabyte. This represents an increase of approx. 2 to 3 orders of magnitude in the amount of data accumulated by present HEP experiments. The system is called the HEPDBC System (High Energy Physics Database Computing System). At present, the Mark 0 HEPDBC System is completed, and can produce analysis of HEP experimental data approx. an order of magnitude faster than current production software on data sets of approx. 1 GB. The Mark 1 HEPDBC System is currently undergoing testing and is designed to analyze data sets 10 to 100 times larger.
Baden, Andrew↗
Neuromorphic overparameterisation and few-shot learning in multilayer physical neural networks
Abstract Physical neuromorphic computing, exploiting the complex dynamics of physical systems, has seen rapid advancements in sophistication and performance. Physical reservoir computing, a subset of neuromorphic computing, faces limitations due to its reliance on single systems. This constrains output dimensionality and dynamic range, limiting performance to a narrow range of tasks. Here, we engineer a suite of nanomagnetic array physical reservoirs and interconnect them in parallel and series to create a multilayer neural network architecture. The output of one reservoir is recorded, scaled and virtually fed as input to the next reservoir. This networked approach increases output dimensionality, internal dynamics and computational performance. We demonstrate that a physical neuromorphic system can achieve an overparameterised state, facilitating meta-learning on small training sets and yielding strong performance across a wide range of tasks. Our approach’s efficacy is further demonstrated through few-shot learning, where the system rapidly adapts to new tasks.
Science & Technology - Other Topics↗
Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging
Computational wave imaging (CWI) extracts hidden structure and physical properties of a volume of material by analyzing wave signals that traverse that volume. Applications include seismic exploration of the Earth’s subsurface, acoustic imaging and nondestructive testing (NDT) in material science, and ultrasound computed tomography (USCT) in medicine. Current approaches for solving CWI problems can be divided into two categories: those rooted in traditional physics and those based on deep learning. Physics-based methods stand out for their ability to provide high-resolution and quantitatively accurate estimates of acoustic properties within the medium. However, they can be computationally intensive and are susceptible to ill-posedness and nonconvexity typical of CWI problems. Machine learning (ML)-based computational methods have recently emerged, offering a different perspective to address these challenges. Diverse scientific communities have independently pursued the integration of deep learning in CWI. This review discusses how contemporary scientific ML techniques, and deep neural networks in particular, have been developed to enhance and integrate with traditional physics-based methods for solving CWI problems. We present a structured framework that consolidates existing research spanning multiple domains, including computational imaging, wave physics, and data science. This study concludes with important lessons learned from existing ML-based methods and identifies technical hurdles and emerging trends through a systematic analysis of the extensive literature on this topic.
42 ENGINEERING↗
The HEPCloud Facility: elastic computing for High Energy Physics – The NOvA Use Case
The need for computing in the HEP community follows cycles of peaks and valleys mainly driven by conference dates, accelerator shutdown, holiday schedules, and other factors. Because of this, the classical method of provisioning these resources at providing facilities has drawbacks such as potential overprovisioning. As the appetite for computing increases, however, so does the need to maximize cost efficiency by developing a model for dynamically provisioning resources only when needed. To address this issue, the HEPCloud project was launched by the Fermilab Scientific Computing Division in June 2015. Its goal is to develop a facility that provides a common interface to a variety of resources, including local clusters, grids, high performance computers, and community and commercial Clouds. Initially targeted experiments include CMS and NOvA, as well as other Fermilab stakeholders. In its first phase, the project has demonstrated the use of the “elastic” provisioning model offered by commercial clouds, such as Amazon Web Services. In this model, resources are rented and provisioned automatically over the Internet upon request. In January 2016, the project demonstrated the ability to increase the total amount of global CMS resources by 58,000 cores from 150,000 cores - a 25 percent increase - in preparation for the Recontres de Moriond. In March 2016, the NOvA experiment has also demonstrated resource burst capabilities with an additional 7,300 cores, achieving a scale almost four times as large as the local allocated resources and utilizing the local AWS s3 storage to optimize data handling operations and costs. NOvA was using the same familiar services used for local computations, such as data handling and job submission, in preparation for the Neutrino 2016 conference. In both cases, the cost was contained by the use of the Amazon Spot Instance Market and the Decision Engine, a HEPCloud component that aims at minimizing cost and job interruption. This paper describes the Fermilab HEPCloud Facility and the challenges overcome for the CMS and NOvA communities.
Fuess, S.↗
Quantum Computing for High-Energy Physics: State of the Art and Challenges
Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage—namely, a significant (in some cases exponential) speedup of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small-scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models that are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this Roadmap paper, led by CERN, DESY, and IBM, we provide the status of high-energy physics quantum computations and give examples of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
On the stability constants of metal–nitrate complexes in aqueous solutions
Stability constants of simple reactions involving addition of the NO 3 − ion to hydrated metal complexes, [M(H 2 O) x ] n + are calculated with a computational workflow developed using cloud computing resources.
Dinpajooh, Mohammadhasan [Physical & Computational↗
Some physical and numerical aspects of computing the effects of viscosity on fluid flow
Some physical, analytical, and computational aspects of viscous flow are examined with reference to examples of computed flows. The discussion of the physical aspects covers the development of important scales used to reference flow phenomena in laminar and turbulent shear layers; the usefulness of the concepts of circulation and vorticity; and some relatively large-scale organized structures that have recently been identified in transitional and fully developed turbulent flows. Among the analytical aspects discussed are a compact presentation of the compressible Navier-Stokes equations, the Reynolds-averaged form of these equations, and a simplified description of some forms of turbulent models. Finally, results of a simulation of the onset of transition, direct turbulence simulations, and large-eddy simulations are given.
Lomax, H.↗
Self-consistent field theory simulations of polymers on arbitrary domains
We introduce a framework for simulating the mesoscale self-assembly of block copolymers in arbitrary confined geometries subject to Neumann boundary conditions. We employ a hybrid finite difference/volume approach to discretize the mean-field equations on an irregular domain represented implicitly by a level-set function. The numerical treatment of the Neumann boundary conditions is sharp, i.e. it avoids an artificial smearing in the irregular domain boundary. This strategy enables the study of self-assembly in confined domains and enables the computation of physically meaningful quantities at the domain interface. In addition, we employ adaptive grids encoded with Quad-/Oc-trees in parallel to automatically refine the grid where the statistical fields vary rapidly as well as at the boundary of the confined domain. This approach results in a significant reduction in the number of degrees of freedom and makes the simulations in arbitrary domains using effective boundary conditions computationally efficient in terms of both speed and memory requirement. Finally, in the case of regular periodic domains, where pseudo-spectral approaches are superior to finite differences in terms of CPU time and accuracy, we use the adaptive strategy to store chain propagators, reducing the memory footprint without loss of accuracy in computed physical observables.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Maximum Kolmogorov-Sinai Entropy Versus Minimum Mixing Time in Markov Chains
We establish a link between the maximization of Kolmogorov Sinai entropy (KSE) and the minimization of the mixing time for general Markov chains. Since the maximisation of KSE is analytical and easier to compute in general than mixing time, this link provides a new faster method to approximate the minimum mixing time dynamics. It could be interesting in computer sciences and statistical physics, for computations that use random walks on graphs that can be represented as Markov chains.
97 MATHEMATICS AND COMPUTING↗
Quantum logic gate synthesis as a Markov decision process
Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming. The underlying assumption is that those tasks could be modeled as Markov decision processes (MDPs). Here, we investigate the feasibility of this assumption by exploring its consequences for single-qubit quantum state preparation and gate compilation. By forming discrete MDPs, we solve for the optimal policy exactly through policy iteration. We find optimal paths that correspond to the shortest possible sequence of gates to prepare a state or compile a gate, up to some target accuracy. Our method works in both the absence and presence of noise and compares favorably to other quantum compilation methods, such as the Ross–Selinger algorithm. This work provides theoretical insight into why reinforcement learning may be successfully used to find optimally short gate sequences in quantum programming.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Axisymmetric simulations of vertical displacement events in tokamaks: A benchmark of M3D-C 1 , NIMROD, and JOREK
A benchmark exercise for the modeling of vertical displacement events (VDEs) is presented and applied to the 3D nonlinear magnetohydrodynamic codes M3D-C 1 , JOREK, and NIMROD. The simulations are based on a vertically unstable NSTX equilibrium enclosed by an axisymmetric resistive wall with a rectangular cross section. A linear dependence of the linear VDE growth rates on the resistivity of the wall is recovered for sufficiently large wall conductivity and small temperatures in the open field line region. We report the benchmark results show good agreement between the VDE growth rates obtained from linear NIMROD and M3D-C 1 simulations and from the linear phase of axisymmetric nonlinear JOREK, NIMROD, and M3D-C 1 simulations. Axisymmetric nonlinear simulations of a full VDE performed with the three codes are compared, and an excellent agreement is found regarding the plasma location and plasma currents, as well as eddy and halo currents in the wall.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
A new paradigm for petascale Monte Carlo simulation: Replica exchange Wang-Landau sampling
We introduce a generic, parallel Wang-Landau method that is naturally suited to implementation on massively parallel, petaflop supercomputers. The approach introduces a replica-exchange framework in which densities of states for overlapping sub-windows in energy space are determined iteratively by traditional Wang-Landau sampling. The advantages and general applicability of the method are demonstrated for several distinct systems that possess discrete or continuous degrees of freedom, including those with complex free energy landscapes and topological constraints.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Magnetic Materials at finite Temperatures: thermodynamics and combined spin and molecular dynamics derived from first principles calculations
We present a unified approach to describe the combined behavior of the atomic and magnetic degrees of freedom in magnetic materials. Using Monte Carlo simulations directly combined with first principles the Curie temperature can be obtained ab initio in good agreement with experimental values. The large scale constrained first principles calculations have been used to construct effective potentials for both the atomic and magnetic degrees of freedom that allow the unified study of influence of phonon-magnon coupling on the thermodynamics and dynamics of magnetic systems. The MC calculations predict the specific heat of iron in near perfect agreement with experimental results from 300K to above Tc and allow the identification of the importance of the magnon-phonon interaction at the phase-transition. Further Molecular Dynamics and Spin Dynamics calculations elucidate the dynamics of this coupling and open the potential for quantitative and predictive descriptions of dynamic structure factors in magnetic materials using first principles derived simulations.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Plasma Paradigm Shift Document for the CoCoMANS Project
This is a dynamic document in the Computational Co-design of Multi-scale Applications in the Natural Sciences (CoCoMANS) LANL LDRD DR project. As of the current date we are approximately half way through this three year project and we have made significant prototype-app and mini-app progress in our plasma application. We are progressing towards developing compact-app software and using this software to demonstrate a paradigm shift in computational plasma physics via an evolving computational co-design process.
97 MATHEMATICS AND COMPUTING↗
Variational Integration for Ideal MHD with Built-in Advection Equations
Newcomb's Lagrangian for ideal MHD in Lagrangian labeling is discretized using discrete exterior calculus. Variational integrators for ideal MHD are derived thereafter. Besides being symplectic and momentum preserving, the schemes inherit built-in advection equations from Newcomb's formulation, and therefore avoid solving them and the accompanying error and dissipation. We implement the method in 2D and show that numerical reconnection does not take place when singular current sheets are present. We then apply it to studying the dynamics of the ideal coalescence instability with multiple islands. The relaxed equilibrium state with embedded current sheets is obtained numerically.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
The Ristra project: FY20/21 milestone report [Slides]
Abstract not provided.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗