13 citations · 13 across the 3 of their papers we have counts for
3 papers
Probabilistic Inverse Modeling of Contaminant Transport via a Conditioned-on-Design Bayesian Physics Informed Neural Network
Milad Panahi, Giovanni Michele Porta, Monica Riva +1
We address the inverse problem of reactive transport in heterogeneous porous media, where unknown model parameters must be inferred from sparse experimental observations. The probl…
Physics Informed Differentiable Solvers for Learning Parametric Solution Manifolds in Heterogeneous Physical Systems
Milad Panahi, Giovanni Michele Porta, Monica Riva +1
Learning the full family of solutions to parameterized partial differential equations (PDEs) is a central challenge to our ability to model the behavior of heterogeneous systems, w…
Modelling parametric uncertainty in PDEs models via Physics-Informed Neural Networks
Milad Panahi, Giovanni Michele Porta, Monica Riva +1
We provide an approach enabling one to employ physics-informed neural networks (PINNs) for uncertainty quantification. Our approach is applicable to systems where observations are…