activity
20172026
most citedFinite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

363 citations · 637 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2026

LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems

Kuangdai Leng, Simon Jeffery, Panos Panagos +1

Understanding soil is fundamental to agriculture, carbon cycling, and environmental sustainability, yet progress is limited by fragmented and heterogeneous datasets that constrain…

physics.geo-ph2025

Agroseismology: unraveling the impact of farming practices on soil hydrodynamics

Qibin Shi, David R. Montgomery, Abigail L. S. Swann +9

Farmed landscapes provide a natural laboratory to test how management reshapes near-surface hydrodynamics. Combining distributed acoustic sensing with physics-based hydromechanical…

physics.geo-ph2024

Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-Concept

Sam A. Scivier, Tarje Nissen-Meyer, Paula Koelemeijer +1

Estimates of seismic wave speeds in the Earth (seismic velocity models) are key input parameters to earthquake simulations for ground motion prediction. Owing to the non-uniqueness…

physics.comp-ph2021★ 363 cited

Finite Basis Physics-Informed Neural Networks (FBPINNs): a scalable domain decomposition approach for solving differential equations

Ben Moseley, Andrew Markham, Tarje Nissen-Meyer

Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm for solving problems relating to differential equations. Compared to classical numerical met…

physics.comp-ph2020★ 114 cited

Solving the wave equation with physics-informed deep learning

Ben Moseley, Andrew Markham, Tarje Nissen-Meyer

We investigate the use of Physics-Informed Neural Networks (PINNs) for solving the wave equation. Whilst PINNs have been successfully applied across many physical systems, the wave…

physics.geo-ph2018★ 116 cited

Fast approximate simulation of seismic waves with deep learning

Benjamin Moseley, Andrew Markham, Tarje Nissen-Meyer

We simulate the response of acoustic seismic waves in horizontally layered media using a deep neural network. In contrast to traditional finite-difference modelling techniques our…

physics.geo-ph2017★ 44 cited

Seismic wave propagation in icy ocean worlds

Simon C. Stähler, Mark P. Panning, Steve D. Vance +4

Seismology was developed on Earth and shaped our model of the Earth's interior over the 20th century. With the exception of the Philae lander, all in situ extraterrestrial seismolo…