Earthquakes threaten communities and critical infrastructure, yet current wave simulation tools remain too slow and resource-intensive for rapid hazard analysis and large scenario studies, and they struggle to fully leverage the growing scale of seismic sensor data.
NeurDE aims to make acoustic wave modeling faster without giving up the physical reliability needed for hazard assessment, scenario screening, and data-driven seismology.
We pair the Lattice Boltzmann Method with a learned component that improves the most failure-prone step, while keeping the core transport process grounded in physics, establishing a hybrid approach between numerical solvers and end-to-end learned models (PINNs, FNOs, DPOT, etc.).