Neural networks have recently gained attention in solving inverse problems. One prominent methodology are Physics-Informed Networks (PINNs) which can solve both forward and In the paper at hand, full waveform inversion is considered problem. The performance of PINNs compared against classical adjoint optimization, focusing on three key aspects: forward-solver, neural network Ansatz for field, s...

1 References If you are using FEAST, please cite the following publication:

The problem of determining the shape an object from knowledge far-field a single incident wave in two-dimensional elasticity was considered. We applied iterative hybrid method to tackle this problem. An advantage is that it does not need forward solver, and therefore, exact boundary condition essential. By deriving Fréchet derivatives two operators, we established reconstruction algorithms for ...

We present a new Simplex-based linear arithmetic solver that can be integrated efficiently in the DPLL(T ) framework. The new solver improves over existing approaches by enabling fast backtracking, supporting a priori simplification to reduce the problem size, and providing an efficient form of theory propagation. We also present a new and simple approach for solving strict inequalities. Experi...