11 citations · 13 across the 3 of their papers we have counts for
3 papers
Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
Monika Nagy-Huber, Volker Roth
Partial differential equations (PDEs) are widely used to describe relevant phenomena in dynamical systems. In real-world applications, we commonly need to combine formal PDE models…
Mesh-free Eulerian Physics-Informed Neural Networks
Fabricio Arend Torres, Marcello Massimo Negri, Monika Nagy-Huber +2
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…
Learning Invariances with Generalised Input-Convex Neural Networks
Vitali Nesterov, Fabricio Arend Torres, Monika Nagy-Huber +2
Considering smooth mappings from input vectors to continuous targets, our goal is to characterise subspaces of the input domain, which are invariant under such mappings. Thus, we w…