Learning Invariances with Generalised Input-Convex Neural Networks

no code implementations • 14 Apr 2022 • Vitali Nesterov, Fabricio Arend Torres, Monika Nagy-Huber, Maxim Samarin, Volker Roth

These networks represent functions that are guaranteed to have connected level sets forming smooth manifolds on the input space.

Mesh-free Eulerian Physics-Informed Neural Networks

no code implementations • 3 Jun 2022 • Fabricio Arend Torres, Marcello Massimo Negri, Monika Nagy-Huber, Maxim Samarin, Volker Roth

Physics-informed Neural Networks (PINNs) have recently emerged as a principled way to include prior physical knowledge in form of partial differential equations (PDEs) into neural networks.

Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations

no code implementations • 18 Aug 2023 • Monika Nagy-Huber, Volker Roth

Partial differential equations (PDEs) can describe many relevant phenomena in dynamical systems.