Discovering New Interpretable Conservation Laws as Sparse Invariants

05/31/2023
by   Ziming Liu, et al.
0

Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the Sparse Invariant Detector (SID), an algorithm that auto-discovers conservation laws from differential equations. Its algorithmic simplicity allows robustness and interpretability of the discovered conserved quantities. We show that SID is able to rediscover known and even discover new conservation laws in a variety of systems. For two examples in fluid mechanics and atmospheric chemistry, SID discovers 14 and 3 conserved quantities, respectively, where only 12 and 2 were previously known to domain experts.

READ FULL TEXT

page 11

page 12

research
11/09/2020

AI Poincaré: Machine Learning Conservation Laws from Trajectories

We present AI Poincaré, a machine learning algorithm for auto-discoverin...
research
02/09/2023

Discovering interpretable Lagrangian of dynamical systems from data

A complete understanding of physical systems requires models that are ac...
research
03/23/2022

AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations

We present a machine learning algorithm that discovers conservation laws...
research
11/02/2018

Discovering conservation laws from data for control

Conserved quantities, i.e. constants of motion, are critical for charact...
research
02/08/2021

Discovering conservation laws from trajectories via machine learning

Invariants and conservation laws convey critical information about the u...
research
10/01/2022

FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities

Many real-world dynamical systems are associated with first integrals (a...
research
08/31/2022

Discovering Conservation Laws using Optimal Transport and Manifold Learning

Conservation laws are key theoretical and practical tools for understand...

Please sign up or login with your details

Forgot password? Click here to reset