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Explore a physics-constrained AI framework for understanding cancer cell migration, enhancing research in radiology and biomedical engineering.
Explore Los Alamos National Laboratory's history, mission, and Theoretical Division's role in advancing national security through multidisciplinary research and cutting-edge computational tools.
Explore machine learning models for efficient electronic structure calculations in materials discovery and drug design, focusing on density functional theory and the Materials Learning Algorithms library.
Explore multi-scale modeling techniques for predicting material performance in nuclear energy applications, focusing on INL's approach using the MOOSE framework.
Explore exascale computing advancements, best practices, and future trends in high-performance computing. Learn strategies for developing portable, performant code and adapting algorithms for heterogeneous architectures.
Explore CHGNet, a graph-neural-network-based machine-learning potential for modeling universal potential energy surfaces in large-scale simulations with complex electron interactions.
Explore DIRECT sampling for robust machine learning interatomic potentials, enhancing accuracy in materials simulations and enabling reliable extrapolation to unseen structures.
Explore a groundbreaking mathematical framework for turbulent fluid motion, covering energy spectra, instability mechanisms, and analytical solutions for turbulence in confined spaces.
Discover equivariances in datasets using LieGAN, a framework for automatic symmetry discovery. Learn to improve prediction accuracy and generalization in scientific applications.
Explore advanced sampling techniques combining generalized Hamiltonian Monte Carlo and delayed rejection for efficient multiscale distribution sampling, overcoming limitations of standard HMC methods.
Explore Clifford Group Equivariant Neural Networks, an innovative E(n)-equivariant method using geometric algebras. Learn about Clifford algebra, its applications, and implementing algorithms in multi-dimensional spaces.
Explore advanced geometric volume of fluid methods for tracking sub-grid interfacial features in multiphase flow simulations, including physics-based models for topology changes.
Explore cutting-edge cosmological simulations of galaxy formation, focusing on the THESAN project's insights into the epoch of re-ionization and early Universe.
Explore computational design methodologies for deployable structures, combining geometry, simulation, and optimization to create innovative material systems with multiple geometric states.
Explore cutting-edge computational approaches in weather and climate modeling, including high-resolution trends, digital twins, and emerging paradigms for advanced simulations.
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