Recent decades have seen a rise in the use of physics methods to study different societal
phenomena. This development has been due to physicists venturing outside of their …

We present AI Poincaré, a machine learning algorithm for autodiscovering conserved
quantities using trajectory data from unknown dynamical systems. We test it on five …

Applications of neural networks to condensed matter physics are becoming popular and
beginning to be well accepted. Obtaining and representing the ground and excited state …

Invariants and conservation laws convey critical information about the underlying dynamics
of a system, yet it is generally infeasible to find them from large-scale data without any prior …

Time-reversal symmetry, which requires that the dynamics of a system should not change
with the reversal of time axis, is a fundamental property that frequently holds in classical and …

We introduce a methodology for seeking conservation laws within a Hamiltonian dynamical
system, which we term “neural deflation.” Inspired by deflation methods for steady states of …

Invariants and conservation laws convey critical information about the underlying dynamics
of a system, yet it is generally infeasible to find them from large-scale data without any prior …

With respect to knowledge-dependent approaches (KDAs) that require optimization in the
high-dimensional parameter space, data-driven methods (DDMs) show remarkable …

This paper investigates the super-resolution of velocity fields in two-dimensional flows from
the viewpoint of rotational equivariance. Super-resolution refers to techniques that enhance …

We introduce the use of neural networks as classifiers on classical disordered systems with
no spatial ordering. In this study, we propose a framework of design objectives for learning …