The Physics-Informed Neural Network Gravity Model: Generation III

• URL: http://arxiv.org/abs/2312.10257v3
• Date: Mon, 03 Feb 2025 20:39:01 GMT
• Title: The Physics-Informed Neural Network Gravity Model: Generation III
• Abstract summary: Third generation Physics-Informed Neural Network Gravity Model (PINN-GM-III)<n>This paper introduces the third generation Physics-Informed Neural Network Gravity Model (PINN-GM-III)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scientific machine learning and the advent of the Physics-Informed Neural Network (PINN) have shown high potential in their ability to solve complex differential equations. One example is the use of PINNs to solve the gravity field modeling problem -- learning convenient representations of the gravitational potential from position and acceleration data. These PINN gravity models, or PINN-GMs, have demonstrated advantages in model compactness, robustness to noise, and sample efficiency when compared to popular alternatives; however, further investigation has revealed various failure modes for these and other machine learning gravity models which this manuscript aims to address. Specifically, this paper introduces the third generation Physics-Informed Neural Network Gravity Model (PINN-GM-III) which includes design changes that solve the problems of feature divergence, bias towards low-altitude samples, numerical instability, and extrapolation error. Six evaluation metrics are proposed to expose these past pitfalls and illustrate the PINN-GM-III's robustness to them. This study concludes by evaluating the PINN-GM-III modeling accuracy on a heterogeneous density asteroid, and comparing its performance to other analytic and machine learning gravity models.

Related papers

• Comparing Physics-Informed and Neural ODE Approaches for Modeling Nonlinear Biological Systems: A Case Study Based on the Morris-Lecar Model [0.0]
  This study systematically evaluates the performance of PINNs and NODEs across three canonical bifurcation regimes.<n>Results indicate that PINNs tend to achieve higher accuracy and robustness in scenarios involving stiffness or sensitive bifurcations.
  arXiv  Detail & Related papers  (2026-03-27T18:53:53Z)
• Uncertainties in Physics-informed Inverse Problems: The Hidden Risk in Scientific AI [0.0]
  We propose a framework to quantify and analyze uncertainties in the estimation of coefficient functions in PIML.<n>We apply our framework to reduced model of magnetohydrodynamics and our framework shows that there are uncertainties, and unique identification is possible with geometric constraints.
  arXiv  Detail & Related papers  (2025-11-06T17:20:02Z)
• Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution [3.4393226199074114]
  We propose physics-informed neural networks (PINNs) for simultaneous numerical model error approximation and superresolution. PINNs effectively predict model errors in both x and y displacement fields with small differences between predictions and ground truth. Our findings demonstrate that the integration of physics-informed loss functions enables neural networks (NNs) to surpass a purely data-driven approach for approximating model errors.
  arXiv  Detail & Related papers  (2024-11-14T17:03:09Z)
• Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks [0.0]
  This paper explores the use of Physics-Informed Neural Networks (PINNs) for the identification and estimation of dynamical systems. PINNs offer a unique advantage by embedding known physical laws directly into the neural network's loss function, allowing for simple embedding of complex phenomena. The results demonstrate that PINNs deliver an efficient tool across all aforementioned tasks, even in presence of modelling errors.
  arXiv  Detail & Related papers  (2024-10-02T08:58:30Z)
• Physics-Informed Deep Learning of Rate-and-State Fault Friction [0.0]
  We develop a multi-network PINN for both the forward problem and for direct inversion of nonlinear fault friction parameters. We present the computational PINN framework for strike-slip faults in 1D and 2D subject to rate-and-state friction. We find that the network for the parameter inversion at the fault performs much better than the network for material displacements to which it is coupled.
  arXiv  Detail & Related papers  (2023-12-14T23:53:25Z)
• SimPINNs: Simulation-Driven Physics-Informed Neural Networks for Enhanced Performance in Nonlinear Inverse Problems [0.0]
  This paper introduces a novel approach to solve inverse problems by leveraging deep learning techniques. The objective is to infer unknown parameters that govern a physical system based on observed data.
  arXiv  Detail & Related papers  (2023-09-27T06:34:55Z)
• Capturing dynamical correlations using implicit neural representations [85.66456606776552]
  We develop an artificial intelligence framework which combines a neural network trained to mimic simulated data from a model Hamiltonian with automatic differentiation to recover unknown parameters from experimental data. In doing so, we illustrate the ability to build and train a differentiable model only once, which then can be applied in real-time to multi-dimensional scattering data.
  arXiv  Detail & Related papers  (2023-04-08T07:55:36Z)
• Learning Physical Dynamics with Subequivariant Graph Neural Networks [99.41677381754678]
  Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization. Our model achieves on average over 3% enhancement in contact prediction accuracy across 8 scenarios on Physion and 2X lower rollout MSE on RigidFall.
  arXiv  Detail & Related papers  (2022-10-13T10:00:30Z)
• Human Trajectory Prediction via Neural Social Physics [63.62824628085961]
  Trajectory prediction has been widely pursued in many fields, and many model-based and model-free methods have been explored. We propose a new method combining both methodologies based on a new Neural Differential Equation model. Our new model (Neural Social Physics or NSP) is a deep neural network within which we use an explicit physics model with learnable parameters.
  arXiv  Detail & Related papers  (2022-07-21T12:11:18Z)
• Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture [77.59766598165551]
  Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation. Here, we propose Auto-PINN, which employs Neural Architecture Search (NAS) techniques to PINN design. A comprehensive set of pre-experiments using standard PDE benchmarks allows us to probe the structure-performance relationship in PINNs.
  arXiv  Detail & Related papers  (2022-05-27T03:24:31Z)
• Neural Operator with Regularity Structure for Modeling Dynamics Driven by SPDEs [70.51212431290611]
  Partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics. We propose the Neural Operator with Regularity Structure (NORS) which incorporates the feature vectors for modeling dynamics driven by SPDEs. We conduct experiments on various of SPDEs including the dynamic Phi41 model and the 2d Navier-Stokes equation.
  arXiv  Detail & Related papers  (2022-04-13T08:53:41Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles [8.689056739160593]
  This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models. We show that a PGRNN can improve prediction accuracy over that of physics-based models, while generating outputs consistent with physical laws. Although we present and evaluate this methodology in the context of modeling the dynamics of temperature in lakes, it is applicable more widely to a range of scientific and engineering disciplines.
  arXiv  Detail & Related papers  (2020-01-28T15:44:45Z)

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.