A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics

• URL: http://arxiv.org/abs/2604.07129v1
• Date: Wed, 08 Apr 2026 14:21:47 GMT
• Title: A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics
• Abstract summary: We introduce AegirJAX, a differentiable hydrodynamic solver based on the depth-integrated, non-static shallow-water equations.<n>AegirJAX treats the time-marching physics loop as a continuous computational graph.<n>We demonstrate the framework's versatility across a suite of scientific machine learning tasks.
• Score: 4.010598744735379
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Numerical simulation of wave propagation and run-up is a cornerstone of coastal engineering and tsunami hazard assessment. However, applying these forward models to inverse problems, such as bathymetry estimation, source inversion, and structural optimization, remains notoriously difficult due to the rigidity and high computational cost of deriving discrete adjoints. In this paper, we introduce AegirJAX, a fully differentiable hydrodynamic solver based on the depth-integrated, non-hydrostatic shallow-water equations. By implementing the solver entirely within a reverse-mode automatic differentiation framework, AegirJAX treats the time-marching physics loop as a continuous computational graph. We demonstrate the framework's versatility across a suite of scientific machine learning tasks: (1) discovering regime-specific neural corrections for model misspecifications in highly dispersive wave propagation; (2) performing continuous topology optimization for breakwater design; (3) training recurrent neural networks in-the-loop for active wave cancellation; and (4) inverting hidden bathymetry and submarine landslide kinematics directly from downstream sensor data. The proposed differentiable paradigm fundamentally blurs the line between forward simulation and inverse optimization, offering a unified, end-to-end framework for coastal hydrodynamics.

Related papers

• Floating-Body Hydrodynamic Neural Networks [8.501171043928354]
  We propose a physics-structured framework that predicts interpretable parameters such as directional added masses, drag coefficients, and a streamfunction-based flow, and couples them with analytic equations of motion.<n>Compared with Hamiltonian and Lagrangian neural networks, FHNN more effectively handles dissipative dynamics while preserving interpretability, which bridges the gap between black-box learning and transparent system identification.
  arXiv  Detail & Related papers  (2025-09-17T07:51:35Z)
• Self-Supervised Coarsening of Unstructured Grid with Automatic Differentiation [55.88862563823878]
  In this work, we present an original algorithm to coarsen an unstructured grid based on the concepts of differentiable physics.<n>We demonstrate performance of the algorithm on two PDEs: a linear equation which governs slightly compressible fluid flow in porous media and the wave equation.<n>Our results show that in the considered scenarios, we reduced the number of grid points up to 10 times while preserving the modeled variable dynamics in the points of interest.
  arXiv  Detail & Related papers  (2025-07-24T11:02:13Z)
• Learnable-Differentiable Finite Volume Solver for Accelerated Simulation of Flows [40.06403338393609]
  We propose a learnable and differentiable finite volume solver, called LDr, for efficient and accurate simulation of fluid flows on coarse grids.<n>Experiments show that LDr achieves efficiency, surpassing baseline models with notable margins.
  arXiv  Detail & Related papers  (2025-06-23T14:22:27Z)
• Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences [1.740114131842074]
  Accurate vessel trajectory prediction is crucial for navigational safety, route optimization, traffic management, search and rescue operations, and autonomous navigation.<n>Traditional data-driven models lack real-world physical constraints, leading to forecasts that disobey vessel motion dynamics.<n>We propose a Physics-Informed Neural Network (PINN) approach for trajectory prediction that integrates a streamlined kinematic model for vessel motion into the neural network training process.
  arXiv  Detail & Related papers  (2025-05-22T15:09:25Z)
• AI-Enhanced Automatic Design of Efficient Underwater Gliders [60.45821679800442]
  Building an automated design framework is challenging due to the complexities of representing glider shapes and the high computational costs associated with modeling complex solid-fluid interactions.<n>We introduce an AI-enhanced automated computational framework designed to overcome these limitations by enabling the creation of underwater robots with non-trivial hull shapes.<n>Our approach involves an algorithm that co-optimizes both shape and control signals, utilizing a reduced-order geometry representation and a differentiable neural-network-based fluid surrogate model.
  arXiv  Detail & Related papers  (2025-04-30T23:55:44Z)
• Ambient Noise Full Waveform Inversion with Neural Operators [11.44207799108199]
  Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods.<n>In this study, we demonstrate the first application of neural operators for full waveform inversion on a real seismic dataset.
  arXiv  Detail & Related papers  (2025-03-19T09:10:43Z)
• Improving Pseudo-Time Stepping Convergence for CFD Simulations With Neural Networks [44.99833362998488]
  Navier-Stokes equations may exhibit a highly nonlinear behavior. The system of nonlinear equations resulting from the discretization of the Navier-Stokes equations can be solved using nonlinear iteration methods, such as Newton's method. In this paper, pseudo-transient continuation is employed in order to improve nonlinear convergence.
  arXiv  Detail & Related papers  (2023-10-10T15:45:19Z)
• Machine learning for phase-resolved reconstruction of nonlinear ocean wave surface elevations from sparse remote sensing data [37.69303106863453]
  We propose a novel approach for phase-resolved wave surface reconstruction using neural networks. Our approach utilizes synthetic yet highly realistic training data on uniform one-dimensional grids.
  arXiv  Detail & Related papers  (2023-05-18T12:30:26Z)
• Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management [64.17887333976593]
  Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. We use differentiable programming with a full-physics model and machine learning to determine the fluid extraction rates that prevent over-pressurization.
  arXiv  Detail & Related papers  (2022-06-21T20:38:13Z)
• Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations [69.5454078868963]
  Navier-Stokes equations are significant partial differential equations that describe the motion of fluids such as liquids and air. With the development of AI techniques, several approaches have been designed to integrate deep neural networks in simulating and inferring the fluid dynamics governed by incompressible Navier-Stokes equations. We propose the emphDeep Random Vortex Method (DRVM), which combines the neural network with a random vortex dynamics system equivalent to the Navier-Stokes equation.
  arXiv  Detail & Related papers  (2022-06-20T04:58:09Z)
• Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models [23.480913364381664]
  Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. We present a novel, fully differentiable hybrid approach to FSI that combines a 2D numerical simulation for the deformable solid structure of the swimmer. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer.
  arXiv  Detail & Related papers  (2022-03-30T15:21:44Z)
• Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics [4.547988283172179]
  We explore the use of Neural Ordinary Differential Equations for fluid flow simulation. Test problems we consider include incompressible flow around a cylinder and real-world applications of shallow water hydrodynamics in riverine and estuarine systems. Our findings indicate that Neural ODEs provide an elegant framework for stable and accurate evolution of latent-space dynamics with a promising potential of extrapolatory predictions.
  arXiv  Detail & Related papers  (2021-04-22T19:20:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.