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At least 1,549 records · Page 86

Natural switches in behaviour rapidly modulate hippocampal coding

Throughout their daily lives, animals and humans often switch between different behaviours. However, neuroscience research typically studies the brain while the animal is performing one behavioural task at a time, and little is known about how brain circuits represent switches between different behaviours. Here we tested this question using an ethological setting: two bats flew together in a long 135 m tunnel, and switched between navigation when flying alone (solo) and collision avoidance as they flew past each other (cross-over). Bats increased their echolocation click rate before each cross-over, indicating attention to the other bat. Hippocampal CA1 neurons represented the bat’s own position when flying alone (place coding). Notably, during cross-overs, neurons switched rapidly to jointly represent the interbat distance by self-position. This neuronal switch was very fast—as fast as 100 ms—which could be revealed owing to the very rapid natural behavioural switch. The neuronal switch correlated with the attention signal, as indexed by echolocation. Interestingly, the different place fields of the same neuron often exhibited very different tuning to interbat distance, creating a complex non-separable coding of position by distance. Theoretical analysis showed that this complex representation yields more efficient coding. Overall, our results suggest that during dynamic natural behaviour, hippocampal neurons can rapidly switch their core computation to represent the relevant behavioural variables, supporting behavioural flexibility.

60 APPLIED LIFE SCIENCES↗

A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media

Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372: a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.

3D images↗

Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data

The design space for inertial confinement fusion (ICF) experiments is vast, and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called “transfer learning,” the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models are trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. Here, we demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility and predict the outcome of recent experiments with less than 10% error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product—models that become increasingly accurate as data are acquired.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Global gyrokinetic simulations of ASDEX Upgrade up to the transport timescale with GENE–Tango

An accurate description of turbulence up to the transport timescale is essential for predicting core plasma profiles and enabling reliable calculations for designing advanced scenarios and future devices. Here, we exploit the gap separation between turbulence and transport timescales and couple the global gyrokinetic code GENE to the transport-solver Tango, including kinetic electrons, collisions, realistic geometries, toroidal rotation and electromagnetic effects for the first time. This approach overcomes gyrokinetic codes' limitations and enables high-fidelity profile calculations in experimentally relevant plasma conditions, significantly reducing the computational cost. We present numerical results of GENE–Tango for two ASDEX Upgrade discharges, one of which exhibits a pronounced peaking of the ion temperature profile not reproduced by TGLF–ASTRA. We show that GENE–Tango can correctly capture the ion temperature peaking observed in the experiment. By retaining different physical effects in the GENE simulations, e.g., collisions, toroidal rotation and electromagnetic effects, we show that the ion temperature profile's peaking can be linked to electromagnetic effects of submarginal (stable) KBM modes. Furthermore, based on these results, the expected GENE–Tango speedup for the ITER standard scenario is larger than two orders of magnitude compared to a single gyrokinetic simulation up to the transport timescale, possibly making first-principles ITER simulations feasible on current computing resources.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Probe microscopy is all you need *

We pose that microscopy offers an ideal real-world experimental environment for the development and deployment of active Bayesian and reinforcement learning methods. Indeed, the tremendous progress achieved by machine learning (ML) and artificial intelligence over the last decade has been largely achieved via the utilization of static data sets, from the paradigmatic MNIST to the bespoke corpora of text and image data used to train large models such as GPT3, DALL·E and others. However, it is now recognized that continuous, minute improvements to state-of-the-art do not necessarily translate to advances in real-world applications. We argue that a promising pathway for the development of ML methods is via the route of domain-specific deployable algorithms in areas such as electron and scanning probe microscopy and chemical imaging. This will benefit both fundamental physical studies and serve as a test bed for more complex autonomous systems such as robotics and manufacturing. Favorable environment characteristics of scanning and electron microscopy include low risk, extensive availability of domain-specific priors and rewards, relatively small effects of exogenous variables, and often the presence of both upstream first principles as well as downstream learnable physical models for both statics and dynamics. Recent developments in programmable interfaces, edge computing, and access to application programming interfaces (APIs) facilitating microscope control, all render the deployment of ML codes on operational microscopes straightforward. We discuss these considerations and hope that these arguments will lead to create novel set of development targets for the ML community by accelerating both real world ML applications and scientific progress.

47 OTHER INSTRUMENTATION↗

HAPPA: A Modular Platform for HPC Application Resilience Analysis with LLMs Embedded

High-performance computing (HPC) systems are increasingly vulnerable to soft errors, which pose significant challenges in maintaining computational accuracy and reliability. Predicting the resilience of HPC applications to these errors is crucial for robust code protection and detailed resilience analysis. In this study, we present HAppA, a modular platform designed for HPC Application Resilience Analysis. Embedding Large Language Models (LLMs), HAppA addresses understanding the context information of long code sequences typical in HPC applications. HAppA implements a novel code representation module that chunks the code into fixed-size segments and aggregates the embeddings of these segments. Three aggregation methods have been explored: MeanPooling, MaxPooling, and LSTM-based techniques. We built a DAtaset for REsilience analysis using Fault Injection (FI), named DARE. Using our DARE dataset, HAppA is trained for regression prediction tasks. Our evaluation results demonstrate the predictive accuracy of HAppA compared to other models, particularly noting that the LSTM-based aggregation method -- HAppA-LSTM -- achieves a mean squared error (MSE) of 0.078 for SDC prediction, surpassing the existing state-of-the-art PARIS model, which recorded an MSE of 0.1172. Additionally, HAppA with the KeyBERT model extracts a list of keywords representing the source code. A comprehensive importance analysis of these keywords further elucidates the code patterns contributing to the error rate. These findings highlight the effectiveness of HAppA in analyzing the resilience of HPC applications and establish a new benchmark for predictive accuracy in resilience.

Jiang, Hailong [Kent State University]↗

Athena - Digital Twin and Infrastructure Model [SWR 20-106]

The software provides database access and analysis code for project ATHENA data. In particular, it provides (1) access to the AWS RDS database and (2) supporting code for analysis. Additionally, the software contains all the code related to modeling the Dallas-Fort Worth International Airport (DFW) curbside (CTA modeling) using SUMO simulator. It demonstrates how to install, run and derive useful data from SUMO simulations. We will describe the basic files needed to run a simulation and how those files are generated. The software also has code for generating simulations representing different traffic management policies for DFW and simulating those policies on personal computer or on NREL's Eagle supercomputer. The examples provided are a representation of the DFW airport for curbside dropoff and and pickup.

Severino, Joseph↗

Development of a high-fidelity multi-physics coupling between MCNP6.2 and CTF4.0 for VVER applications

Ensuring system safety in the design, licensing, and operation phases is a priority in the nuclear industry. Performing extensive, full-scale reactor safety experiments is often prohibitive due to the large associated costs. Computational simulations offer an alternative safety analysis method, typically with significant cost reductions. Recent high-level developments in technology and increased availability of computational resources have allowed the development of high-fidelity, high-resolution multi-physics coupled codes. Such developments may be used to generate reference models for deterministic core calculations. Under this framework, the high-fidelity continuous energy Monte Carlo-based neutron transport code MCNP6.2 was coupled externally with the state-of-the-art thermal-hydraulics subchannel code, CTF4.0 for VVER (Water-Water Energetic Reactor) applications. A VVER-1000 fuel assembly model was used to demonstrate the capability of the coupled code. The converged coupled solution is compared to initial results, consisting of the first MCNP evaluation and first CTF evaluation after initialization. The VVER-1000 type assembly results are compared to other evaluations of the same assembly model. The findings indicate good agreement with expectations and reference cases, where available. The initial results of the coupled MCNP6.2/CTF4.0 calculations at the assembly level for steady-state calculations are presented in this study, which may support future work toward high-fidelity coupled full-core modeling capabilities. The multi-physics model may be further improved, enhanced, and expanded for both cycle depletion and transient applications. Such a multi-physics system will also be applicable to the VVER-1200 and other triangular lattice designs and support their deployment and operation safely and economically. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Initial full core SMR simulations with NekRS

This document describes the completion of a recent milestone of the ExaSMR program concerning "Full core simulations". The ExaSMR project is developing tools for coupling Monte Carlo (MC) radiation transport solvers to a computational fluid dynamics (CFD) solver. Work is based on the Shift MC, OpenMC MC, and Nek5000/NekRS CFD codes. As part of this milestone, a novel set of pin-resolved CFD full-core simulations have been performed for the first time. These simulations represent a significant increase in capability in what is now possible with CFD on pre-Exascale systems. The simulations have been performed with the spectral element solvers NekRS on the supercomputer Summit. Multiple simulations campaigns have been conducted: (1) LES simulations in bare bundle, (2) RANS simulations in bare bundles, (3) RANS simulations with momentum sources and (4) Conjugate heat transfer calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Dislocation Patterning in Deforming Crystals: Theory, Computational Predictions and Validation (Final Technical Report)

This project was awarded for an initial period of three years, followed by a no-cost extension for one year and a funded one-year renewal. During the first four years, the project focused on investigating the role of dislocation reactions and dislocation correlation in dislocation patterning in FCC metals. During the fifth year, the scope of research was expanded to investigate the effects of composition inhomogeneity on the mesoscale plastic response of Body-Centered Cubic (BCC) alloys. In addition to its impact on metal hardening during deformation, dislocation patterning provides the microstructure information required to understand phenomena like fracture and recrystallization in metals. The Continuum Dislocation Dynamics (CDD) framework was the methodology used during the first four years, followed by the use of Discrete Dislocation Dynamics (DDD) during the fifth year. CDD is a density-based formalism of dislocation dynamics in which the plastic deformation of crystals is predicted together with the mesoscale dislocation patterns by tracking the space-time evolution of dislocations as driven by the applied stress, short-range and long-range interactions of dislocations, and cross slip. CDD is a crystal mechanics approach in which the plastic constitutive part is replaced with the equations of dislocation dynamics, which is driven by the internal stress via a mobility laws, while the evolution of the density gives the eigenstrain required to update the internal stress itself. The governing equations are thus those of crystal mechanics cast as an eigenstrain problem and those of dislocation transport and reactions. Our investigations during the first four years were driven by the hypothesis that that dislocation patterning is triggered by spatiotemporal dislocation density and internal stress fluctuations, and that such fluctuations influence the collective dislocation dynamic through their effects on the short-range reaction rates and the collective dislocation mobility. This hypothesis was tested by modeling the influence of the dislocation reactions and dislocation correlations on collective dislocation dynamics. The main research components during the first four years were: 1) Reformulation of the CDD framework to integrate the dislocation correlations and dislocation reactions. 2) Simulation of the dislocation correlations and quantifying their contribution to the long-range stress of complex dislocation systems. 3) Computational implementation of the updated CDD model for FCC single crystals and development of an efficient CDD code. 4) Investigation of dislocation patterning in FCC crystals based on the updated CDD model. Some key findings from these investigations were: 1) The patterning of dislocations is initiated by cross slip and internal stress fluctuations, and the refinement of the pattern results from junction formation. 2) Patterning is more prominent under high stress. 3) The correlation stress was found to be a significant part of the mean field stress in continuum representations of dislocation dynamics. During the last year of the project, we have established a method for performing dislocation dynamics in inhomogeneous alloys and demonstrated the impact of composition inhomogeneity on the characteristics of initial yielding and dislocation character in BCC alloys. We also tested this method using an irradiated ferritic alloy in which the composition and dislocation loops effects are both present. In doing so, we have considered two aspects of the role of inhomogeneity, the creation of internal coherency stress due to the dependence of the lattice parameter on the local composition, and the dependence of the dislocation mobility on the local composition. These two aspects resulted in an unexpected behavior of the dislocation system, and, in turn, the yielding behavior of BCC alloys. Key findings include: 1) The composition inhomogeneity in BCC alloys alters the well-known role of screw dislocations by forcing a level of waviness on the average dislocation character, thus destroying the screw character dominance in the yielding observed in pure or homogeneous BCC metals. 2) In the case of irradiated alloys, while composition inhomogeneity by itself and dislocation loops by themselves result in some hardening, the superposition of the two mechanisms does not result in the sum of the two contributions via any known rule. The latter result contradicts the classical works related hardening via multiple mechanisms, which state that various hardening mechanisms can be added linearly or using some Pythagorean additive rule. We were able to rationalize this unexpected result by a closer loop at the origin of the hardening mechanisms themselves, which both involved long-range elastic interactions with dislocations. We reached the conclusion that the hardening resulting from the total stress associated with these two mechanisms (current work) differs from the sum of the effects of the individual stress fields (classical literature). We have also found a major flaw in the classical theory of spinodal hardening. The latter theory never considered the impact of composition undulation on the solute hardening. We have built a new general theory accounting for the effect of solute friction together with the composition undulations on the dislocation configuration at Critical Resolved Shear Stress (CRSS), which is yielding a new definition of the spinodal hardening and new different results. This work is the first to recognize the lack of the role of solute in spinodal hardening theory of alloys.

36 MATERIALS SCIENCE↗

Developing Platinum-Group-Metal-Free Catalysts for Oxygen Reduction Reaction in Acid: Beyond the Single Metal Site

This project is to develop M (x) -N-C catalysts with dense multiple metal center (MMC) sites to meet the DOE 2025 activity target of 0.044 mA/cm 2 at 0.9 V IR-free ., as well as other goals such as durability. We have made important contributions to both catalyst development and fundamental understandings of the active sites in M-N-C catalysts in this project. We successfully made M (x) -N-C catalysts with some multiple metal center (MMC) sites by combining ionothermal carbonization with chemical vapor deposition (CVD). These catalysts, however, are not as active as the most active single-atom Fe-N-C catalysts made by the similar CVD process, owing likely to the low site density and the presence of inorganic Fe species such as iron carbides and nanoparticles. The most significant accomplishments we achieved in this project are: (1) we unraveled the formation pathway of Fe-N 4 sites during pyrolysis step-by-step and identified the trans-metalation mechanism, in collaboration with Deborah Myers and her colleagues at Argonne National Laboratory (ANL); (2) inspired by this finding, we pioneered the CVD synthesis of M-N-C catalysts (M = Mn, Fe, and Co), in which the Fe-N-C catalyst by CVD demonstrated an ORR activity of 33 mA/cm 2 at 0.9 V in H 2 -O 2 proton exchange membrane fuel cells (PEMFCs), very close to the ultimate goal of 35 mA/cm 2 of our project. This catalyst is the first Fe-N-C catalyst that contains only D1 sites without the D2 sites; whereas D1 and D2 sites have been always identified by Mossbauer in previous Fe-N-C catalysts. This finding helps to understand what the D1 and D2 sites are and their roles in catalyzing the ORR. (3) by improving the mass transport of the carbon matrix prior to the CVD process, the revised Fe-N-C catalyst made by CVD delivered a maximum power density of 0.53 W/cm 2 in H 2 -air PEMFCs. The improvement strategy was partly inspired by the computational modeling work by Adam Weber from LBNL, the Co-PI of this project, by developing, coding, and exercising a continuum level model of transport phenomena within a PGM-free catalyst layer. The model demonstrated that local resistances combined with limited site density of the PGM-free catalyst can result in limiting currents and poor polarization performance. The model also gave design guidance for impact of ECSA and overall catalyst-layer thickness. However, both Fe-N-C and Co-N-C catalysts developed by CVD showed poor durability in PEMFCS, in comparison with the traditional M-N-C catalysts synthesized via regular pyrolysis process. Consequently, we did not achieve the proposed durability targets. Despite so, we believe the FeNC-CVD catalysts with the poor durability and D1 sites only provides an excellent platform to understand the degradation mechanism of Fe-N-C in PEMFCs, the most important challenge in the development of M-N-C catalysts.

08 HYDROGEN↗

Integrated Security-Safety Consequence Study of a Heat Pipe Reactor

This report presents a preliminary demonstration of integrated security/safety consequence modeling to support potential physical security exemption justifications under the new Title 10 Code of Federal Regulations Part 53 licensing pathway, which allows exemptions if sabotage-induced radiological consequences do not exceed 25 rem at the site boundary two hours after release without mitigative actions. A hypothetical heat pipe microreactor subjected to direct sabotage by a 150 lbs. TNT-equivalent high-explosive device was analyzed using a simplified radionuclide release estimate based on DOE-HDBK-3010-94, implemented in the MELCOR severe accident analysis code to model release transport and attenuation through reactor/building compartments, with MELCOR outputs coupled to MACCS to compute offsite doses and the distance-to-threshold metric D 25rem . Six scenarios were evaluated to examine sensitivities to release pathway/building configuration and radionuclide form (vapor versus aerosols with different size distributions). Results show a positive correlation between leak path factor and D 25rem and indicate that reactor building compartmentalization can substantially reduce consequences (a single-story configuration reduced D 25rem by roughly an order of magnitude relative to a cavity-only configuration), while vapor versus aerosol assumptions had limited impact for the modeled two-story case on the spatial grid used.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Mahakala: A Python-based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Spacetimes

We introduce Mahakala, a Python-based, modular, radiative ray-tracing code for curved spacetimes. We employ Google's JAX framework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr spacetimes. JAX also enables Mahakala to run in parallel on both CPUs and GPUs. Mahakala natively uses the Cartesian Kerr–Schild coordinate system, which avoids numerical issues caused by the pole in spherical coordinate systems. We demonstrate Mahakala's capabilities by simulating 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature of Mahakala allows us to quantitatively explore how different regions of the flow influence different image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole and peaks near the photon orbit. We also quantify the relative contribution of the disk, forward jet, and counterjet to 1.3 mm images.

79 ASTRONOMY AND ASTROPHYSICS↗

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Heterogeneous systems are becoming increasingly prevalent. In order to exploit the rich compute resources of such systems, robust programming models are needed for application developers to seamlessly migrate legacy code from today’s systems to tomorrow’s. Over the past decade and more, directives have been established as one of the promising paths to tackle programmatic challenges on emerging systems. This work focuses on applying and demonstrating OpenMP offloading directives on five proxy applications. We observe that the performance varies widely from one compiler to the other; a crucial aspect of our work is reporting best practices to application developers who use OpenMP offloading compilers. While some issues can be worked around by the developer, there are other issues that must be reported to the compiler vendors. By restructuring OpenMP offloading directives, we gain an 18x speedup for the su3 proxy application on NERSC’s Cori system when using the Clang compiler, and a 15.7x speedup by switching max reductions to add reductions in the laplace mini-app when using the Cray-llvm compiler on Cori.

Davis, Josh↗

Compiler directed fine grained power management

Systems, methods, devices, and computer-implemented instructions for processor power management implemented in a compiler. In some implementations, a characteristic of code is determined. An instruction based on the determined characteristic is inserted into the code. The code and inserted instruction are compiled to generate compiled code. The compiled code is output.

Bharadwaj, Vedula Venkata Srikant↗

Rancor-HUNTER: A Virtual Plant and Operator Environment for Predicting Human Performance

Advances in simulation capabilities to model physical systems have outpaced the development of simulations for humans using those physical systems. There is an argument that the infinite span of potential human behaviors inherently render human modeling more challenging than physical systems. Despite this challenge, the need for modeling humans interacting with these complex systems is paramount. As technologies have improved, many of the failure modes originating from the physical systems have been solved. This means the overall proportion of human errors has increased, such that it is not uncommon to be the primary driver of system failure in modern complex systems. Moreover, technologies such as automated systems may introduce emerging contexts that can cause new, unanticipated modes of human error. Therefore, it is now more important than ever to develop models of human behavior to realize overall system error reductions and achieve established safety margins. To support new and novel concepts of operations for the anticipated wave of advanced nuclear reactor deployments, human factors and human reliability analysis researchers need to develop advanced simulation-based approaches. This talk presents a simulation environment suitable to both collect data and then perform Monte Carlo simulations to evaluate human performance and develop better models of human behavior. Specifically, the Rancor Microworld Simulator models a complex energy production system in a simplified manner. Rancor includes computer-based procedures, which serve as a framework to automatically classify human behaviors without manual, subjective experimenter coding during scenarios. This method supports a detailed level of analysis at the task level. It is feasible for collecting large sample sizes required to develop quantitative modelling elements that have historically challenged traditional full-scope simulator study approaches. Additionally, the other portion of this experimental platform, the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), is presented to show how the collected data can be used to evaluate novel scenarios based on the contextual factors, or performance shaping factors, derived from Rancor simulations. Rancor-HUNTER is being used to predict operator performance with new procedures, such as results from control room modernization or new-build situations. Rancor-HUNTER is also proving a useful surrogate platform to model human performance for other complex systems.

99 GENERAL AND MISCELLANEOUS↗

Code Generators for Floating-Point Unit Design in Integrated Circuits (OpenFloat) v1.0

This IP provides a comprehensive set of code generators for various floating-point units (FPUs) essential for integrated circuit design and integration, targeting a broad spectrum of applications, including machine learning and scientific computing. The suite includes FP adders, multipliers, subtractors, dividers, reciprocals, exponentials, square roots, trigonometric functions (sine, cosine, arctangent), and more. It supports customizable hardware design parameters, such as precision (16, 32, 64, and 128 bits) and pipeline depths, offering users enhanced flexibility and productivity. The generated code is in an industry-standard hardware description language, ensuring compatibility with standard design flows, including simulation, verification, synthesis, and implementation on both field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs).

Shalf, JohnM. [Lawrence Berkeley National Laborato↗

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗