Copertina

Pi theorem formulation of flood mapping

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Bartlett, Mark S., Van Blitterswyk, Jared, Farella, Martha, Li, Jinshu, Smith, Curtis, Parolari, Anthony J., Krishnamoorthy, Lalitha, Mrad, Assaad
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Geophysics Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
di: Radfar, Soheil, et al.
Pubblicazione: (2025)

Dargana: fine-tuning EarthPT for dynamic tree canopy mapping from space
di: Smith, Michael J., et al.
Pubblicazione: (2025)

End-to-End Mineral Exploration with Artificial Intelligence and Ambient Noise Tomography
di: Muir, Jack, et al.
Pubblicazione: (2024)

EarthPT: a time series foundation model for Earth Observation
di: Smith, Michael J., et al.
Pubblicazione: (2023)

A Framework for Modeling Liquefaction-Induced Road Disruptions After Earthquakes: Implications for Emergency Response and Access in the Cascadia Region of North America
di: Sanger, Morgan D., et al.
Pubblicazione: (2026)

Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements
di: Kanin, E. A., et al.
Pubblicazione: (2022)

Direct Estimation of Porosity from Seismic Data using Rock and Wave Physics Informed Neural Networks (RW-PINN)
di: Vashisth, Divakar, et al.
Pubblicazione: (2022)

Bayesian full waveform inversion with sequential surrogate model refinement
di: Meles, Giovanni Angelo, et al.
Pubblicazione: (2025)

Discovery and inversion of the viscoelastic wave equation in inhomogeneous media
di: Chen, Su, et al.
Pubblicazione: (2024)

Reconstructing Historical Climate Fields With Deep Learning
di: Bochow, Nils, et al.
Pubblicazione: (2023)

Inspired by machine learning optimization: can gradient-based optimizers solve cycle skipping in full waveform inversion given sufficient iterations?
di: Mu, Xinru, et al.
Pubblicazione: (2025)

Ambient Noise Full Waveform Inversion with Neural Operators
di: Zou, Caifeng, et al.
Pubblicazione: (2025)

Feature-based Inversion of 2.5D Controlled Source Electromagnetic Data using Generative Priors
di: Zhou, Hongyu, et al.
Pubblicazione: (2026)

HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
di: Wang, Yihan, et al.
Pubblicazione: (2025)

Sensoformer: Robust Sim-to-Real Inference on Variable-Geometry Sensor Sets via Physics-Structured Randomization
di: Jia, Zhe, et al.
Pubblicazione: (2026)

In-context learning enables continental-scale subsurface temperature prediction from sparse local observations
di: O'Malley, Daniel, et al.
Pubblicazione: (2026)

A Critical Assessment of PINNs and Operator Learning for Geotechnical Engineering
di: Kumar, Krishna
Pubblicazione: (2025)

Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion
di: Yu, Ziye, et al.
Pubblicazione: (2026)

Controllable seismic velocity synthesis using generative diffusion models
di: Wang, Fu, et al.
Pubblicazione: (2024)

First observations of the seiche that shook the world
di: Monahan, Thomas, et al.
Pubblicazione: (2024)

Physics-informed waveform inversion using pretrained wavefield neural operators
di: Huang, Xinquan, et al.
Pubblicazione: (2025)

WaveCastNet: Rapid Wavefield Forecasting for Earthquake Early Warning via Deep Sequence to Sequence Learning
di: Lyu, Dongwei, et al.
Pubblicazione: (2024)

Enforcing Reciprocity in Operator Learning for Seismic Wave Propagation
di: Zou, Caifeng, et al.
Pubblicazione: (2026)

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization
di: Bo, Zhenkai, et al.
Pubblicazione: (2026)

Transcending Classical Neural Network Boundaries: A Quantum-Classical Synergistic Paradigm for Seismic Data Processing
di: Yuan, Zhengyi, et al.
Pubblicazione: (2026)

Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage
di: Ju, Xin, et al.
Pubblicazione: (2026)

Seismic Traveltime Tomography with Label-free Learning
di: Wang, Feng, et al.
Pubblicazione: (2024)

Explainable AI models for predicting liquefaction-induced lateral spreading
di: Hsiao, Cheng-Hsi, et al.
Pubblicazione: (2024)

Physics-Trained Neural Network as Inverse Problem Solver for Potential Fields: An Example of Downward Continuation between Arbitrary Surfaces
di: Sun, Jing, et al.
Pubblicazione: (2025)

Investigating the effect of CPT in lateral spreading prediction using Explainable AI
di: Hsiao, Cheng-Hsi, et al.
Pubblicazione: (2025)

Diffusion-based subsurface CO$_2$ multiphysics monitoring and forecasting
di: Huang, Xinquan, et al.
Pubblicazione: (2024)

Learning Generative Models for Lumped Rainfall-Runoff Modeling
di: Yang, Yang, et al.
Pubblicazione: (2023)

HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
di: Bagagli, Matteo, et al.
Pubblicazione: (2025)

Physics-Informed Linear Model (PILM): Analytical Representations and Application to Crustal Strain Rate Estimation
di: Okazaki, Tomohisa
Pubblicazione: (2025)

Fine Flood Forecasts: Incorporating local data into global models through fine-tuning
di: Ryd, Emil, et al.
Pubblicazione: (2025)

Approaches for enhancing extrapolability in process-based and data-driven models in hydrology
di: Shi, Haiyang
Pubblicazione: (2024)

Towards Operational Streamflow Forecasting in the Limpopo River Basin using Long Short-Term Memory Networks
di: Tlhomole, James, et al.
Pubblicazione: (2026)

Parametric modeling of shear wave velocity profiles for the conterminous U.S
di: Sanger, Morgan D., et al.
Pubblicazione: (2025)

HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL
di: Li, Zhi, et al.
Pubblicazione: (2026)

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
di: Huang, Xinquan, et al.
Pubblicazione: (2023)