Machine Learning, Scientific Method, Numerical Methods, Numerical Analysis, Neural Networks.
inverse problems that can be tackled with ADCME is The forward model must be differentiable, i.e., ∂F/∂X and ∂F/∂θ​ exist. However, we do not require those gradients to be implemented by users; they can be computed with automatic differentiation in ADCME. The forward model must be a white-box and