Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? — mentioned by quanto

Mon, 17 Feb 2025 19:18:43 GMT

The key intuition is calculating the loss function without actually knowing the exact solution ("labels" in supervised learning parlance). Note that this is not unique to PINN: there are existing numerical methods that do exactly this. I used to solve PDEs for a living; and my academic background is in numerical solutions to PDEs before going into ML. In my industry and academic experience, PINN is a novel curiosity with perhaps niche applications that I am not as familiar with. Yes, I am aware of works of Bruton, Duvenaud et al (and was even in the same lab group with some of them). I am…

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Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? · HackerPapers discussions

Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? — mentioned by quanto