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Maryam Toloubidokhti

2 accepted papers

2024

DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics-Informed Neural Networks

ICLR 2024poster

Advancements in deep learning have led to the development of physics-informed neural networks (PINNs) for solving partial differential equations (PDEs) without being supervised by PDE solutions. While vanilla PINNs require training one network per PDE configuration, recent works have showed the pote…

Cited by 8SourcePDFScholar
2024

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution

NeurIPS 2024poster

The interest in leveraging physics-based inductive bias in deep learning has resulted in recent development of _hybrid deep generative models (hybrid-DGMs)_ that integrates known physics-based mathematical expressions in neural generative models. To identify these hybrid-DGMs requires inferring par…

Cited by 0SourcePDFScholar