Pde-refiner: Achieving accurate long rollouts with neural pde solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in science and
engineering. Recently, mostly due to the high computational cost of traditional solution …
engineering. Recently, mostly due to the high computational cost of traditional solution …
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks
In recent years, advances in computing hardware and computational methods have
prompted a wealth of activities for solving inverse problems in physics. These problems are …
prompted a wealth of activities for solving inverse problems in physics. These problems are …
Modeling accurate long rollouts with temporal neural PDE solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in science and
engineering. Recently, mostly due to the high computational cost of traditional solution …
engineering. Recently, mostly due to the high computational cost of traditional solution …
[HTML][HTML] FPL-net: A deep learning framework for solving the nonlinear Fokker–Planck–Landau collision operator for anisotropic temperature relaxation
The nonlinear collision operator consumes a significant amount of computation time in
tokamak whole-volume modeling, and in current numerical methods, the computational time …
tokamak whole-volume modeling, and in current numerical methods, the computational time …
Spectral-Refiner: Fine-Tuning of Accurate Spatiotemporal Neural Operator for Turbulent Flows
Recent advancements in operator-type neural networks have shown promising results in
approximating the solutions of spatiotemporal Partial Differential Equations (PDEs) …
approximating the solutions of spatiotemporal Partial Differential Equations (PDEs) …
PAPM: A Physics-aware Proxy Model for Process Systems
In the context of proxy modeling for process systems, traditional data-driven deep learning
approaches frequently encounter significant challenges, such as substantial training costs …
approaches frequently encounter significant challenges, such as substantial training costs …
[PDF][PDF] Fast and Accurate Simulation of Deformable Solid Dynamics on Coarse Meshes
MK Venturelli, W Celes - Available at SSRN 4879043, 2024 - maxwell.vrac.puc-rio.br
Matheus Kerber Venturelli Fast and Accurate Simulation of Deformable Solid Dynamics on
Coarse Meshes Page 1 Matheus Kerber Venturelli Fast and Accurate Simulation of …
Coarse Meshes Page 1 Matheus Kerber Venturelli Fast and Accurate Simulation of …
Differentiable Programming for Computational Plasma Physics
NB McGreivy - 2024 - search.proquest.com
Differentiable programming allows for derivatives of functions implemented via computer
code to be calculated automatically. These derivatives are calculated using automatic …
code to be calculated automatically. These derivatives are calculated using automatic …