3 papers
physics.comp-ph2026
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.comp-ph2026
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…
physics.data-an2024
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…