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Results for “Optimization Under Uncertainty”

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At least 973 records · Page 54

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Quantified Gamow shell model interaction for p s d -shell nuclei

Background: The structure of weakly bound and unbound nuclei close to particle drip lines is one of the major science drivers of nuclear physics. A comprehensive understanding of these systems goes beyond the traditional configuration interaction approach formulated in the Hilbert space of localized states (nuclear shell model) and requires an open quantum system description. The complex-energy Gamow shell model (GSM) provides such a framework as it is capable of describing resonant and nonresonant many-body states on equal footing. Purpose: To make reliable predictions, quality input is needed that allows for the full uncertainty quantification of theoretical results. In this study, we carry out the optimization of an effective GSM (one-body and two-body) interaction in the psdf-shell-model space. The resulting interaction is expected to describe nuclei with 5 ≤ A ≲ 12 at the p-sd-shell interface. Method: The one-body potential of the 4 He core is modeled by a Woods-Saxon + spin-orbit + Coulomb potential, and the finite-range nucleon-nucleon interaction between the valence nucleons consists of central, spin-orbit, tensor, and Coulomb terms. The GSM is used to compute key fit observables. The χ 2 optimization is performed using the Gauss-Newton algorithm augmented by the singular value decomposition technique. The resulting covariance matrix enables quantification of statistical errors within the linear regression approach. Results: The optimized one-body potential reproduces nucleon- 4 He scattering phase shifts up to an excitation energy of 20 MeV. The two-body interaction built on top of the optimized one-body field is adjusted to the bound and unbound ground-state binding energies and selected excited states of the helium, lithium, and beryllium isotopes up to A = 9 . A very good agreement with experimental results was obtained for binding energies. First applications of the optimized interaction include predictions for two-nucleon correlation densities and excitation spectra of light nuclei with quantified uncertainties. In conclusion: The new interaction will enable comprehensive and fully quantified studies of structure and reactions aspects of nuclei from the psd region of the nuclear chart.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Uncertainty and Sensitivity Analysis of Reactivity Insertion Accidents using BISON Fuel Performance Code

The development of Accident Tolerant Fuels (ATF) has expanded into a considerable research program in the wake of the events at the Japanese Fukushima Daiichi nuclear power plant in 2011. The ATF goal is to insert a test assembly into a commercial reactor by 2022. To support this, testing of the ATF design under off-normal conditions is needed to assess the 'enhanced accident tolerance' capability of the fuel rod design over standard UO{sub 2}-Zircaloy concepts. Transient testing capabilities are necessary for off-normal testing of events such as loss of coolant accidents (LOCA) and reactivity insertion accidents (RIA). A transient testing program has been developed to support the testing of ATF designs. The Transient Reactor Test (TREAT) facility has been chosen as the site to perform the transient testing of the ATF designs. TREAT is a versatile test facility capable of subjecting fuel specimens to various off-normal conditions. Historic TREAT experiments were performed in a variety of experimental test vehicles including static environment capsules, once-through flowing systems, and also recirculating flowing systems. The planned TREAT experiments will be operated under PWR conditions. Therefore, a static capsule vehicle is under development to handle required temperatures and pressures. The static capsule, termed the Static Environment Rodlet Transient Test Apparatus (SERTTA), will be used for irradiation tests in TREAT to subject ATF fuel rodlets to a range of power pulse conditions and environments. To aid in the design and analysis of future tests in TREAT, the fuel performance code BISON is being used. Fuel performance codes capable of accident modeling have been developed for some time and they have shown an ability to reproduce experimental results with some degree of adequacy, but they still require extensive verification and validation. Future experiments in TREAT will add to a limited database of past transient tests performed in transient reactors to support LWR systems. These tests can be used for future verification and validation of fuel performance codes. Understanding the impact of the uncertainty of influential parameters is critical for not only analyzing the results of the experiment but also for modeling and validation of codes. For example, uncertainties associated with the initial manufactured state of the sample, instrument measurements, material property models, and planned experiment conditions can provide important insight into model performance, experimental data, and play an important role in experimental design. The objective of this work is to perform an uncertainty and sensitivity analysis on a simplified RIA case of a standard UO{sub 2}-Zircaloy sample using expected nominal conditions in the SERTTA capsule in TREAT. The BISON fuel performance code is used in conjunction with DAKOTA (Design Analysis Kit for Optimization and Tera-scale Applications) to perform the uncertainty and sensitivity analysis. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS2004

Economic Impact of Water Allocation on Agriculture in the Lower Chattahoochee River Basin

The relative value of irrigation water was assessed for three important crops (corn, cotton, and peanuts) grown in the southeastern United States. A decision tool was developed with the objective of allocating limited available water among competing crops in a manner that would maximize the economic returns to the producers. The methodology was developed and tested for a hypothetical farm located in Henry County, Alabama in the Chattahoochee river basin. Crop yield - soil moisture response functions were developed using Monte Carlo simulated data for cotton, corn, and peanuts. A hydrologic model was employed to simulate runoff over the period of observed rainfall the county to provide inflows to storage facilities that could be used as constraints for the optimal allocation of the available water in the face of the uncertainty of future rainfall and runoff. Irrigation decisions were made on a weekly basis during the critical water deficit period in the region. An economic optimization model was employed with the crop responses, and soil moisture functions to determine the optimum amount of water place on each crop subject to the amount of irrigation water availability and climatic uncertainty. The results indicated even small amounts of irrigation could significantly benefit farmers in the region if applied judiciously. A weekly irrigation sequence was developed that maintained the available water on the crops that exhibited the most significant combination of water sensitivity and cash value.

Limaye, Ashutosh S.↗

DOE OSTI2016

Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization

In this paper, we propose an optimal bidding strategy in the day-ahead market of a microgrid consisting of intermittent distributed generation (DG), storage, dispatchable DG and price responsive loads. The microgrid coordinates the energy consumption or production of its components and trades electricity in both the day-ahead and real-time markets to minimize its operating cost as a single entity. The bidding problem is challenging due to a variety of uncertainties, including power output of intermittent DG, load variation, day-ahead and real-time market prices. A hybrid stochastic/robust optimization model is proposed to minimize the expected net cost, i.e., expected total cost of operation minus total benefit of demand. This formulation can be solved by mixed integer linear programming. The uncertain output of intermittent DG and day-ahead market price are modeled via scenarios based on forecast results, while a robust optimization is proposed to limit the unbalanced power in real-time market taking account of the uncertainty of real-time market price. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, a micro-turbine, a diesel generator, a battery and a responsive load show the advantage of stochastic optimization in addition to robust optimization.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1994

Uncertainty relations as Hilbert space geometry

Precision measurements involve the accurate determination of parameters through repeated measurements of identically prepared experimental setups. For many parameters there is a 'natural' choice for the quantum observable which is expected to give optimal information; and from this observable one can construct an Heinsenberg uncertainty principle (HUP) bound on the precision attainable for the parameter. However, the classical statistics of multiple sampling directly gives us tools to construct bounds for the precision available for the parameters of interest (even when no obvious natural quantum observable exists, such as for phase, or time); it is found that these direct bounds are more restrictive than those of the HUP. The implication is that the natural quantum observables typically do not encode the optimal information (even for observables such as position, and momentum); we show how this can be understood simply in terms of the Hilbert space geometry. Another striking feature of these bounds to parameter uncertainty is that for a large enough number of repetitions of the measurements all V quantum states are 'minimum uncertainty' states - not just Gaussian wave-packets. Thus, these bounds tell us what precision is achievable as well as merely what is allowed.

Braunstein, Samuel L.↗

DOE OSTI2018

Accelerated Discovery of Large Electrostrains in BaTiO 3 ‐Based Piezoelectrics Using Active Learning

Abstract A key challenge in guiding experiments toward materials with desired properties is to effectively navigate the vast search space comprising the chemistry and structure of allowed compounds. Here, it is shown how the use of machine learning coupled to optimization methods can accelerate the discovery of new Pb‐free BaTiO 3 (BTO‐) based piezoelectrics with large electrostrains. By experimentally comparing several design strategies, it is shown that the approach balancing the trade‐off between exploration (using uncertainties) and exploitation (using only model predictions) gives the optimal criterion leading to the synthesis of the piezoelectric (Ba 0.84 Ca 0.16 )(Ti 0.90 Zr 0.07 Sn 0.03 )O 3 with the largest electrostrain of 0.23% in the BTO family. Using Landau theory and insights from density functional theory, it is uncovered that the observed large electrostrain is due to the presence of Sn, which allows for the ease of switching of tetragonal domains under an electric field.

Yuan, Ruihao↗

DOE OSTI2014

SU-E-J-83: Ion Imaging to Better Estimate In-Vivo Relative Stopping Powers Using X-Ray CT Prior-Knowledge Information

Purpose: To reduce uncertainties in relative stopping power (RSP) estimates for ions (alpha and carbon) by using Ion radiographic-imaging and X-ray CT prior-knowledge. Methods: A 36×36 phantom matrix composed of 9 materials with different thicknesses and randomly placed is generated. Theoretical RSPs are calculated using stopping power (SP) data from three references (Janni, ICRU49 and Bischel). We introduced an artificial systematic error (1.5%, 2.5% or 3.5%) and a random error (<0.5%) to the SP to simulated patient ion-range errors present in clinic environment. Carbon/alpha final energy for each RSPs set (theoretical and from CT images) is obtained with a ray-tracing algorithm. A gradient descent (GD) method is used to minimize the difference in exit particle energy, between theory and X-ray CT RSP maps, by iteratively correcting the RSP map from X-ray CT. Once a new set of RSPs is obtained for a direction a new optimization is done over other direction using the RSPs from the previous optimization. Theoretical RSPs are compared with experimental RSPs obtained with Gammex Phantom. Results: Preliminary results show that optimized RSP values can be obtained with smaller uncertainties (<1%) than clinical RSPs (1.5% to 3.5%). Theoretical values from three different references show uncertainties, up to 3% from experimental values. Further investigation will consider prior-knowledge from RSP obtained with CT images and ion radiographies from Monte Carlo Simulations. Conclusion: GD and ray-tracing methods have been implemented to reduce RSP uncertainties from values obtained for clinical treatment. Experimental RSPs will be obtained using carbon/alpha beams to consider the existence of material dependent systematic errors. Based on the results, it is hoped to show that using ray-tracing optimization with ion radiography and prior knowledge on RPSs, treatment planning accuracy and cost-effectiveness can be improved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2013

RECOVERY ACT - Robust Optimization for Connectivity and Flows in Dynamic Complex Networks

The goal of this project was to study robust connectivity and flow patterns of complex multi-scale systems modeled as networks. Networks provide effective ways to study global, system level properties, as well as local, multi-scale interactions at a component level. Numerous applications from power systems, telecommunication, transportation, biology, social science, and other areas have benefited from novel network-based models and their analysis. Modeling and optimization techniques that employ appropriate measures of risk for identifying robust clusters and resilient network designs in networks subject to uncertain failures were investigated in this collaborative multi-university project. In many practical situations one has to deal with uncertainties associated with possible failures of network components, thereby affecting the overall efficiency and performance of the system (e.g., every node/connection has a probability of partial or complete failure). Some extreme examples include power grid component failures, airline hub failures due to weather, or freeway closures due to emergencies. These are also situations in which people, materials, or other resources need to be managed efficiently. Important practical examples include rerouting flow through power grids, adjusting flight plans, and identifying routes for emergency services and supplies, in the event network elements fail unexpectedly. Solutions that are robust under uncertainty, in addition to being economically efficient, are needed. This project has led to the development of novel models and methodologies that can tackle the optimization problems arising in such situations. A number of new concepts, which have not been previously applied in this setting, were investigated in the framework of the project. The results can potentially help decision-makers to better control and identify robust or risk-averse decisions in such situations. Formulations and optimal solutions of the considered problems need to capture uncertainty and risk using appropriate probabilistic, statistical and optimization concepts. The main difficulty arising in addressing these issues is the dramatic increase in the computational complexity of the resulting optimization problems. This project studied novel models and methodologies for risk-averse network optimization- specifically, network design, network flows and cluster detection problems under uncertainty. The approach taken was to incorporate a quantitative risk measure known as conditional value-at-risk that is widely used in financial applications. This approach presents a viable alternate modeling and optimization framework to chance-constrained optimization and mean-variance optimization, one that also facilitates the detection of risk-averse solutions.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2000

Probabilistic Multi-Scale, Multi-Level, Multi-Disciplinary Analysis and Optimization of Engine Structures

Aircraft engines are assemblies of dynamically interacting components. Engine updates to keep present aircraft flying safely and engines for new aircraft are progressively required to operate in more demanding technological and environmental requirements. Designs to effectively meet those requirements are necessarily collections of multi-scale, multi-level, multi-disciplinary analysis and optimization methods and probabilistic methods are necessary to quantify respective uncertainties. These types of methods are the only ones that can formally evaluate advanced composite designs which satisfy those progressively demanding requirements while assuring minimum cost, maximum reliability and maximum durability. Recent research activities at NASA Glenn Research Center have focused on developing multi-scale, multi-level, multidisciplinary analysis and optimization methods. Multi-scale refers to formal methods which describe complex material behavior metal or composite; multi-level refers to integration of participating disciplines to describe a structural response at the scale of interest; multidisciplinary refers to open-ended for various existing and yet to be developed discipline constructs required to formally predict/describe a structural response in engine operating environments. For example, these include but are not limited to: multi-factor models for material behavior, multi-scale composite mechanics, general purpose structural analysis, progressive structural fracture for evaluating durability and integrity, noise and acoustic fatigue, emission requirements, hot fluid mechanics, heat-transfer and probabilistic simulations. Many of these, as well as others, are encompassed in an integrated computer code identified as Engine Structures Technology Benefits Estimator (EST/BEST) or Multi-faceted/Engine Structures Optimization (MP/ESTOP). The discipline modules integrated in MP/ESTOP include: engine cycle (thermodynamics), engine weights, internal fluid mechanics, cost, mission and coupled structural/thermal, various composite property simulators and probabilistic methods to evaluate uncertainty effects (scatter ranges) in all the design parameters. The objective of the proposed paper is to briefly describe a multi-faceted design analysis and optimization capability for coupled multi-discipline engine structures optimization. Results are presented for engine and aircraft type metrics to illustrate the versatility of that capability. Results are also presented for reliability, noise and fatigue to illustrate its inclusiveness. For example, replacing metal rotors with composites reduces the engine weight by 20 percent, 15 percent noise reduction, and an order of magnitude improvement in reliability. Composite designs exist to increase fatigue life by at least two orders of magnitude compared to state-of-the-art metals.

Chamis, Christos C.↗

DOE OSTI2016

Impact of nuclear mass uncertainties on the r process

Here, nuclear masses play a fundamental role in understanding how the heaviest elements in the Universe are created in the r process. We predict r -process nucleosynthesis yields using neutron capture and photodissociation rates that are based on the nuclear density functional theory. Using six Skyrme energy density functionals based on different optimization protocols, we determine for the first time systematic uncertainty bands—related to mass modeling—for r -process abundances in realistic astrophysical scenarios. We find that features of the underlying microphysics make an imprint on abundances especially in the vicinity of neutron shell closures: Abundance peaks and troughs are reflected in trends of neutron separation energy. Further advances in the nuclear theory and experiments, when linked to observations, will help in the understanding of astrophysical conditions in extreme r -process sites.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

MO-A-BRB-00: TG191: Clinical Use of Luminescent Dosimeters

This presentation will highlight the upcoming TG-191 report: Clinical Use of Luminescent Dosimeters. Luminescent dosimetry based on TLD and OSLD is a practical, accurate, and precise technique for point dosimetry in medical physics applications. The charges of Task Group 191 were to detail the methodologies for practical and optimal luminescent dosimetry in a clinical setting. This includes (1) To review the variety of TLD/OSL materials available, including features and limitations of each. (2) To outline the optimal steps to achieve accurate and precise dosimetry with luminescent detectors and to evaluate the uncertainty induced when less rigorous procedures are used. (3) To develop consensus guidelines on the optimal use of luminescent dosimeters for clinical practice. (4) To develop guidelines for special medically relevant uses of TLDs/OSLs (e.g., mixed field i.e. photon/neutron dosimetry, particle beam dosimetry, skin dosimetry). While this report provides general guidelines for arbitrary TLD and OSLD processes, the report, and therefore this presentation, provide specific guidance for TLD-100 (LiF:Ti,Mg) and nanoDot (Al2O3:C) dosimeters because of their prevalence in clinical practice. Learning Objectives: Understand the available dosimetry systems, and basic theory of their operation Understand the range of dose determination methodologies and the uncertainties associated with them Become familiar with special considerations for TLD/OSLD relevant for special clinical situations Learn recommended commissioning and QA procedures for these dosimetry systems.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

MO-A-BRB-01: TG191: Clinical Use of Luminescent Dosimeters

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2026

Optimization-based approaches to control of connected and automated vehicles: Principles, complexities, applications, challenges, and outlook

Safe and optimal motion control for connected and automated vehicles (CAVs) poses a fundamental optimization challenge at the intersection of system complexity, environmental uncertainty, and stringent real-time constraints. Existing surveys address this challenge in isolation – focusing either on specific control techniques or individual uncertainty sources – without providing a unified framework that characterizes the trade-offs among computational tractability, performance verifiability, and adaptive generalization across paradigms. This review addresses that gap by presenting a cohesive analytical framework concentrated on the decision-making and trajectory optimization layers of the CAV autonomy stack. We systematically analyze three major optimization paradigms – first-principles model-based optimization, data-driven methods, and hybrid synergistic architectures – evaluating each against four core complexity axes: problem formulation, constraint handling, optimality guarantees, and robustness. Key applications including platooning, trajectory planning, collision avoidance, and cooperative control are examined to reveal recurring methodological patterns and critical operational constraints that limit real-world performance. Our synthesis identifies verifiable hybrid architectures, incentive-aligned multi-agent cooperation, and hardware-algorithm co-design as the defining research frontiers, and distills a targeted agenda for developing CAV control systems that are simultaneously safe, computationally efficient, and deployable in the full complexity of real-world traffic environments.

Muzahid, Abu Jafar Md [University of Tennessee, Kn↗

NASA NTRS1989

Robust perturbation guidance for the advanced launch system

Real-time optimal trajectory generation for constructing guidance laws seems feasible. However, these deterministic guidance schemes may be sensitive to system and environmental uncertainties. To overcome this deficiency, it is suggested that the accessory problem about an optimal path, which generates a quadratic cost criterion in the perturbed states and control away from the optimal trajectory, be used as the cost criterion. A modified quadratic cost is minimized with respect to the perturbed control but maximized with respect to additive uncertainties in the linearized dynamics and in the linearized measurements. In this way, a robust neighboring optimum scheme is constructed. A perturbed guidance scheme is stated. To illustrate the theory, the second stage of a launch vehicle is considered. An optimal path is generated, and about this path a robust neighboring optimum guidance law is constructed and tested.

Speyer, Jason L.↗

DOE OSTI2024

Identifying Bayesian optimal experiments for uncertain biochemical pathway models

Abstract Pharmacodynamic (PD) models are mathematical models of cellular reaction networks that include drug mechanisms of action. These models are useful for studying predictive therapeutic outcomes of novel drug therapies in silico. However, PD models are known to possess significant uncertainty with respect to constituent parameter data, leading to uncertainty in the model predictions. Furthermore, experimental data to calibrate these models is often limited or unavailable for novel pathways. In this study, we present a Bayesian optimal experimental design approach for improving PD model prediction accuracy. We then apply our method using simulated experimental data to account for uncertainty in hypothetical laboratory measurements. This leads to a probabilistic prediction of drug performance and a quantitative measure of which prospective laboratory experiment will optimally reduce prediction uncertainty in the PD model. The methods proposed here provide a way forward for uncertainty quantification and guided experimental design for models of novel biological pathways.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence.

Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and optimizing, for example, concentrated solar power systems. As there are many uncertainties inherent in such flows, conducting uncertainty quantification analyses is fundamental to improve the predictive capabilities of the numerical simulations. For largescale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as many methods require a large number of high-fidelity, forward model solves. This requirement results in the need for a possibly infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method relies on exploiting the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the QoI statistics while requiring a small number of high-fidelity model evaluations. It also enables efficient computation of sensitivity analyses which highlight that epistemic uncertainty plays an important role in the solution of irradiated, particle-laden turbulent flow.

42 ENGINEERING↗

NASA NTRS1995

Metafitting: Weight optimization for least-squares fitting of PTTI data

For precise time intercomparisons between a master frequency standard and a slave time scale, we have found it useful to quantitatively compare different fitting strategies by examining the standard uncertainty in time or average frequency. It is particularly useful when designing procedures which use intermittent intercomparisons, with some parameterized fit used to interpolate or extrapolate from the calibrating intercomparisons. We use the term 'metafitting' for the choices that are made before a fitting procedure is operationally adopted. We present methods for calculating the standard uncertainty for general, weighted least-squares fits and a method for optimizing these weights for a general noise model suitable for many PTTI applications. We present the results of the metafitting of procedures for the use of a regular schedule of (hypothetical) high-accuracy frequency calibration of a maser time scale. We have identified a cumulative series of improvements that give a significant reduction of the expected standard uncertainty, compared to the simplest procedure of resetting the maser synthesizer after each calibration. The metafitting improvements presented include the optimum choice of weights for the calibration runs, optimized over a period of a week or 10 days.

Douglas, Rob J.↗

DOE OSTI2016

SU-F-T-186: A Treatment Planning Study of Normal Tissue Sparing with Robustness Optimized IMPT, 4Pi IMRT, and VMAT for Head and Neck Cases

Purpose: We performed a retrospective dosimetric comparison study between the robustness optimized Intensity Modulated Proton Therapy (RO-IMPT), volumetric-modulated arc therapy (VMAT), and the non-coplanar 4? intensity modulated radiation therapy (IMRT). These methods represent the most advanced radiation treatment methods clinically available. We compare their dosimetric performance for head and neck cancer treatments with special focus on the OAR sparing near the tumor volumes. Methods: A total of 11 head and neck cases, which include 10 recurrent cases and one bilateral case, were selected for the study. Different dose levels were prescribed to tumor target depending on disease and location. Three treatment plans were created on commercial TPS systems for a novel noncoplanar 4π method (20 beams), VMAT, and RO-IMPT technique (maximum 4 fields). The maximum patient positioning error was set to 3 mm and the maximum proton range uncertainty was set to 3% for the robustness optimization. Line dose profiles were investigated for OARs close to tumor volumes. Results: All three techniques achieved 98% coverage of the CTV target and most photon plans had less than 110% of the hot spots. The RO-IMPT plans show superior tumor dose homogeneity than 4? and VMAT plans. Although RO-IMPT has greater R50 dose spillage to the surrounding normal tissue than 4π and VMAT, the RO-IMPT plans demonstrate better or comparable OAR (parotid, mandible, carotid, oral cavity, pharynx, and etc.) sparing for structures closely abutting tumor targets. Conclusion: The RO-IMPT’s ability of OAR sparing is benchmarked against the C-arm linac based non-coplanar 4π technique and the standard VMAT method. RO-IMPT consistently shows better or comparable OAR sparing even for tissue structures closely abutting treatment target volume. RO-IMPT further reduces treatment uncertainty associated with proton therapy and delivers robust treatment plans to both unilateral and bilateral head and neck cancer patients with desirable treatment time.

60 APPLIED LIFE SCIENCES↗