4 papers
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
Hoang Anh Nguyen, Divakar Vashisth, Ali Tura
Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding. Physics-informed neural networks (PINNs) and…
Seismic inversion using hybrid quantum neural networks
Divakar Vashisth, Rohan Sharma, Tejas Ganesh Iyer +2
Seismic inversion-including post-stack, pre-stack, and full waveform inversion is compute and memory-intensive. Recently, several approaches, including physics-informed machine lea…
Inversion of Magnetotelluric Data using Bayesian Neural Networks
Dhruv Poddar, Rohan Sharma, Divakar Vashisth
Magnetotelluric (MT) inversion is a key technique in geophysics for imaging deep subsurface resistivity structures. However, the inherent ill-posedness and non-uniqueness of invers…
Pre-stack and post-stack seismic inversion using quantum computing
Divakar Vashisth, Rodney Lessard, Tapan Mukerji
Quantum computing harnesses the principles of quantum mechanics to solve problems that are intractable for classical computers. Quantum annealing, a specialized approach within qua…