Complex systems with many interacting nodes are inherently stochastic and best described
by stochastic differential equations. Despite increasing observation data, inferring these …

Extracting consistent statistics between relevant free energy minima of a molecular system is
essential for physics, chemistry, and biology. Molecular dynamics (MD) simulations can aid …

Learning unknown stochastic differential equations (SDEs) from observed data is a
significant and challenging task with applications in various fields. Current approaches often …

We propose a data-driven latent space dynamics identification method (tLaSDI) that embeds
the first and second principles of thermodynamics. The latent variables are learned through …

Macroscopic observables of a system are of keen interest in real applications such as the
design of novel materials. Current methods rely on microscopic trajectory simulations, where …

Symbolic regression plays a crucial role in modern scientific research thanks to its capability
of discovering concise and interpretable mathematical expressions from data. A grand …

Deep neural network approaches show promise in solving partial differential equations.
However, unlike traditional numerical methods, they face challenges in enforcing essential …

Some of the most exciting and impactful open scientific problems have computational
complexity as the limiting factor to an in silico solution, eg high–temperature …