Chaotic systems make long-horizon forecasts difficult because small
pert...
This paper introduces a novel deep-learning-based approach for numerical...
Model stitching (Lenc Vedaldi 2015) is a compelling methodology to c...
Studying the dynamics of open quantum systems holds the potential to ena...
Conservation laws are key theoretical and practical tools for understand...
Symbolic regression is a machine learning technique that can learn the
g...
Identifying the governing equations of a nonlinear dynamical system is k...
Bayesian optimization (BO) is a popular paradigm for global optimization...
Experimental data is often affected by uncontrolled variables that make
...