Physics-Informed Neural Networks: The Case for Small Architectures
Physics-Informed Neural Networks (PINNs) are often significantly overparameterized in research settings. Senior developer Ahmad Wael critiques this trend, showing that for low-frequency PDEs like Burgers’ equation or hyperelasticity, networks can be reduced by up to 400x without losing accuracy. Learn to build leaner, more efficient ML architectures by starting small.