May 5, 2023 · This work addresses the challenge of identifying such subspaces automatically: given as input an energy function for a high-dimensional system, ...

Jul 23, 2023 · This work addresses the challenge of identifying such subspaces automatically: given as input an energy function for a high-dimensional system, ...

We fit neural networks as reduced-order models to learn low-dimensional approximations of complex physical systems. This approach applies to a broad range of ...

Use neural networks to fit low-dimensional subspaces for simulations, with no dataset needed—the method automatically explores the potential energy landscape ...

Fig. 1. We fit neural networks as reduced-order models to learn low-dimensional approximations of complex physical systems. This approach applies to a.

Duration: 2:58
Posted: May 4, 2023

The primary advantage of our formulation is that it ts a subspace using only potential energy function for a system, and does not require a training dataset.

We fit neural networks as reduced-order models to learn low-dimensional approximations of complex physical systems.

Duration: 0:30
Posted: Aug 1, 2023

Sep 11, 2024 · This formulation is effective across a very general range of physical systems; our experiments demonstrate not only nonlinear and very low- ...