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Davide Gallon

2 accepted papers

2026

Physics-informed diffusion models in spectral space

ICML 2026poster

We propose a methodology that combines generative latent diffusion models with physics-informed machine learning to generate solutions of parametric partial differential equations (PDEs) conditioned on partial observations, which includes, in particular, forward and inverse PDE problems. We learn th…

Cited by 0SourceScholar
2025

SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures

NeurIPS 2025poster

We study gradient flows for loss landscapes of fully connected feedforward neural networks with commonly used continuously differentiable activation functions such as the logistic, hyperbolic tangent, softplus or GELU function. We prove that the gradient flow either converges to a critical point or…

Cited by 0SourcecodeScholar