Fields

Pick a domain and keep going deeper: field, subfield, topic. Every step lists the papers at that point or below it. The counts are papers that are accepted or in review; papers that were not accepted are in the full library.

APEBench 0

LagrangeBench 0

Solver-in-the-loop scenarios 0

Learning-to-simulate particle datasets 0

LagrangeBench datasets 0

Kolmogorov flow (generated with JAX-CFD) 0

Solver-in-the-loop 0

Learned interpolation / correction 0

Neural ODE 0

GNS 0

Hamiltonian Neural Networks 0

Lagrangian Neural Networks 0

PDE-Refiner 0

Differentiable-physics control 0

Gradient-quality caveat 0

differentiable physics 0

hybrid solver 0

learned correction 0

adjoint 0

inverse design 0

graph network simulator 0

hamiltonian neural network 0

Subfield

Differentiable simulation and hybrid solver-in-the-loop learning

Benchmarks: APEBench, LagrangeBench, Solver-in-the-loop scenarios. Datasets: Learning-to-simulate particle datasets, LagrangeBench datasets, Kolmogorov flow (generated with JAX-CFD). Baselines: Solver-in-the-loop, Learned interpolation / correction, Neural ODE, GNS and others.

Fields

Differentiable simulation and hybrid solver-in-the-loop learning

No papers here yet