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

Causal representation learning promises to extend causal models to hidden causal
variables from raw entangled measurements. However, most progress has focused on …

In symbolic regression, the objective is to find an analytical expression that accurately fits
experimental data with the minimal use of mathematical symbols such as operators …

Approximating nonlinear differential equations using a neural network provides a robust and
efficient tool for various scientific computing tasks, including real-time predictions, inverse …

In an era where symbolic mathematical equations are indispensable for modeling complex
natural phenomena, scientific inquiry often involves collecting observations and translating …

Technological advancements have substantially increased computational power and data
availability, enabling the application of powerful machine-learning (ML) techniques across …

Many successful methods to learn dynamical systems from data have recently been
introduced. However, ensuring that the inferred dynamics preserve known constraints, such …

The last decade has seen blossoming research in deep learning theory attempting to
answer," Why does deep learning generalize?" A powerful shift in perspective precipitated …

Dynamical systems governed by ordinary differential equations (ODEs) serve as models for
a vast number of natural and social phenomena. In this work, we offer a fresh perspective on …

The promising outcomes of dynamical system identification techniques, such as SINDy
(Brunton et al. in Proc Natl Acad Sci 113 (15): 3932–3937, 2016), highlight their advantages …