Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …

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 …

Effective data-driven PDE forecasting methods often rely on fixed spatial and/or temporal
discretizations. This raises limitations in real-world applications like weather prediction …

Chaotic systems make long-horizon forecasts difficult because small perturbations in initial
conditions cause trajectories to diverge at an exponential rate. In this setting, neural …

Chaotic dynamical systems (DS) are ubiquitous in nature and society. Often we are
interested in reconstructing such systems from observed time series for prediction or …

While complex simulations of physical systems have been widely used in engineering and
scientific computing, lowering their often prohibitive computational requirements has only …

Recent advances in deep learning for physics have focused on discovering shared
representations of target systems by incorporating physics priors or inductive biases into …

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

Machine learning methods can be a valuable aid in the scientific process, but they need to
face challenging settings where data come from inhomogeneous experimental conditions …

A fundamental challenge in physics-informed machine learning (PIML) is the design of
robust PIML methods for out-of-distribution (OOD) forecasting tasks. These OOD tasks …