Efficient physics-informed learning with built-in uncertainty awareness
Pietro Zanotta; Juha Riippi; Alexandre Halbach; Valtteri Lahtinen; Asser Lähdemäki; Andrew Tweedie
Technical contributors
Dr. Ljubomir Budinsky, Dr. Çaǧlar Aytekin, Dr. Valtteri Lahtinen
Key takeaways
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Physics-Informed Neural Networks offer a flexible way to solve PDEs, but scalability remains a challenge
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Separable PINNs reduce the dimensionality burden, yet dense matrix operations still dominate cost
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Quantum Orthogonal SPINNs use quantum circuits as orthogonal quantum layers in the networks to address this bottleneck
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Orthogonality improves stability, regularization, and allows for...