Technical contributors Dr. Ljubomir Budinsky, Dr. Çaǧlar Aytekin, Dr. Valtteri Lahtinen Key takeaways
Physics-Informed Neural Networks offer a flexible way to solve PDEs, but scalability remains a challenge
Separable PINNs reduce the dimensionality burden, yet dense matrix operations still dominate cost
Quantum Orthogonal SPINNs use quantum circuits as orthogonal quantum layers in the networks to address this bottleneck
Orthogonality improves stability, regularization, and allows for...