Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …

Modeling interacting dynamical systems, such as fluid dynamics and intermolecular
interactions, is a fundamental research problem for understanding and simulating complex …

Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …

Solving partial differential equations (PDEs) is the core of many fields of science and
engineering. While classical approaches are often prohibitively slow, machine learning …

This paper studies the problem of modeling interacting dynamical systems, which is critical
for understanding physical dynamics and biological processes. Recent research …

We consider using deep neural networks to solve time-dependent partial differential
equations (PDEs), where multi-scale processing is crucial for modeling complex, time …

Neural networks are emerging as a tool for scalable data-driven simulation of high-
dimensional dynamical systems, especially in settings where numerical methods are …

Predicting physical systems over long-term horizons has a significant challenge. Although
prevalent machine learning techniques, such as Physics-Informed Neural Networks (PINNs) …

Deep learning-based surrogate models have demonstrated remarkable advantages over
classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over …