Feb. 13, 2024, 5:44 a.m. | Petros Ellinas Rahul Nellikath Ignasi Ventura Jochen Stiasny Spyros Chatzivasileiadis

cs.LG updates on arXiv.org arxiv.org

Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accelerating their deployment, especially for safety-critical systems. If successful, such NNs can become integral parts of simulation software tools which can accelerate the simulation of complex dynamic systems more than 100 times. However, the verification of these functions poses major challenges; it is not straightforward how to efficiently bound them or how to represent the derivative of …

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