Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference

• URL: http://arxiv.org/abs/2605.24106v1
• Date: Fri, 22 May 2026 18:14:17 GMT
• Title: Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference
• Abstract summary: Rapid and accurate flood extent mapping from Remote Sensing data is critical for operational disaster response.<n>Standard Deep Learning models often produce physically impossible predictions due to a lack of hydrological constraints.<n>We propose a novel Uncertainty-Aware PINN framework tailored specifically for applied Earth Observation.
• Score: 0.29165586612027233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Rapid and accurate flood extent mapping from Remote Sensing data, such as Synthetic Aperture Radar (SAR), is critical for operational disaster response, but standard Deep Learning models often produce physically impossible predictions due to a lack of hydrological constraints. While PhysicsInformed Neural Networks (PINNs) attempt to address this by embedding governing laws directly into the loss function, their application to real-world remote sensing data frequently fails. Enforcing rigid spatial derivatives (e.g., the 2D Shallow Water Equations) onto unconditioned latent spaces attempting to fit noisy SAR speckle causes catastrophic gradient divergence, a phenomenon we term Physics Shock. In this paper, we propose a novel Uncertainty-Aware PINN framework tailored specifically for applied Earth Observation that addresses this instability. By integrating a dynamic Warm-Start protocol and modeling heteroscedastic aleatoric uncertainty via a negative log-likelihood objective, the network learns to dynamically relax physical constraints in regions of high sensor noise while strictly enforcing them in high-confidence areas. Evaluated on the Sen1Floods11 dataset, our probabilistic Attention-Gated FNO-UNet successfully stabilizes multi-objective optimization, achieving a +25% relative improvement in Intersection over Union (IoU) compared to deterministic baselines. Furthermore, through Deep Ensembles, we successfully disentangle intrinsic sensor noise from out-of-distribution terrain ignorance, providing operational agencies with highly calibrated, physically consistent confidence bounds for robust disaster mitigation and real-time decision-making.

Related papers

• Navigating the Latent Manifold: Proactive Concept Drift Adaptation for Resilient NIDS [1.9678915302433768]
  We propose DriftXpert, a novel NIDS for drift-adaptive detection.<n>In Phase 1, we introduce an unsupervised anomaly metric based on latent manifold deviation.<n>In Phase 2, to mitigate catastrophic forgetting under non-stationary distributions, we design a representation consistency alignment strategy.
  arXiv  Detail & Related papers  (2026-09-08T11:58:12Z)
• Quantifying Uncertainty in Space Debris Capture with Active Tether-Net Systems Caused by Noisy Observations [8.825977169303206]
  An active tether-net system with maneuverable units is one of the promising solutions to this problem.<n>This paper focuses on the first of these two uncertainty sources, and presents a pipeline to propagate and quantify the resulting uncertainty.<n>A high-fidelity simulator and a lower-fidelity surrogate-based environment are used to demonstrate trade-offs between prediction accuracy versus ease of resolving uncertainties.
  arXiv  Detail & Related papers  (2026-05-26T21:14:00Z)
• Uncertainty Estimation for Deep Reconstruction in Actuatic Disaster Scenarios with Autonomous Vehicles [68.82821445619759]
  principled uncertainty quantification is critical for active sensing strategies.<n>Evidential Deep Learning achieves the best reconstruction accuracy and uncertainty calibration across all sensor configurations.<n>These findings support Evidential Deep Learning as the preferred method for uncertainty-aware field reconstruction in real-time autonomous vehicle deployments.
  arXiv  Detail & Related papers  (2026-04-07T19:17:44Z)
• When Sensors Fail: Temporal Sequence Models for Robust PPO under Sensor Drift [64.37959940809633]
  We study robustness of Proximal Policy Optimization (PPO) under temporally persistent sensor failures.<n>We show Transformer-based sequence policies substantially outperform, RNN, and SSMs in robustness, maintaining high returns even when large fractions of sensors are unavailable.
  arXiv  Detail & Related papers  (2026-03-04T22:21:54Z)
• RaUF: Learning the Spatial Uncertainty Field of Radar [2.6853734738584047]
  Millimeter-wave radar offers unique advantages in adverse weather but suffers from low spatial fidelity, severe azimuth ambiguity, and clutter-induced spurious returns.<n>We propose RaUF, a spatial uncertainty field learning framework that models radar measurements through their physically grounded anisotropic properties.<n>To resolve conflicting feature-to-label mapping, we design an anisotropic probabilistic model that learns fine-grained uncertainty.
  arXiv  Detail & Related papers  (2026-03-01T10:10:43Z)
• SourceNet: Interpretable Sim-to-Real Inference on Variable-Geometry Sensor Arrays for Earthquake Source Inversion [7.936803576241796]
  SourceNet is a Transformer-based framework that treats the sensor array as a flexible set to model arbitrary geometries.<n>By pre-training on 100,000 synthetic events and fine-tuning on 2,000 real world events, SourceNet achieves state-of-the-art precision on held-out real data.
  arXiv  Detail & Related papers  (2026-01-09T21:42:28Z)
• Physics-Guided Inductive Spatiotemporal Kriging for PM2.5 with Satellite Gradient Constraints [15.082346657646902]
  High-resolution mapping of fine particulate matter (PM2.5) is a cornerstone of sustainable urbanism but remains critically hindered by the spatial sparsity of ground monitoring networks.<n>This study proposes the Spatio-Guided Inference Network (SPIN), a novel framework designed for inductivetemporal kriging.
  arXiv  Detail & Related papers  (2025-11-20T03:18:41Z)
• AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture [11.644739814142502]
  AquaSentinel is a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks.<n>We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure.
  arXiv  Detail & Related papers  (2025-11-19T20:53:50Z)
• Adaptive Dual Uncertainty Optimization: Boosting Monocular 3D Object Detection under Test-Time Shifts [80.32933059529135]
  Test-Time Adaptation (TTA) methods have emerged to adapt to target distributions during inference.<n>We propose Dual Uncertainty Optimization (DUO), the first TTA framework designed to jointly minimize both uncertainties for robust M3OD.<n>In parallel, we design a semantic-aware normal field constraint that preserves geometric coherence in regions with clear semantic cues.
  arXiv  Detail & Related papers  (2025-08-28T07:09:21Z)
• 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)
• DeepSimHO: Stable Pose Estimation for Hand-Object Interaction via Physics Simulation [81.11585774044848]
  We present DeepSimHO, a novel deep-learning pipeline that combines forward physics simulation and backward gradient approximation with a neural network. Our method noticeably improves the stability of the estimation and achieves superior efficiency over test-time optimization.
  arXiv  Detail & Related papers  (2023-10-11T05:34:36Z)
• Uncertainty-Aware Deep Calibrated Salient Object Detection [74.58153220370527]
  Existing deep neural network based salient object detection (SOD) methods mainly focus on pursuing high network accuracy. These methods overlook the gap between network accuracy and prediction confidence, known as the confidence uncalibration problem. We introduce an uncertaintyaware deep SOD network, and propose two strategies to prevent deep SOD networks from being overconfident.
  arXiv  Detail & Related papers  (2020-12-10T23:28:36Z)
• Attribute-Guided Adversarial Training for Robustness to Natural Perturbations [64.35805267250682]
  We propose an adversarial training approach which learns to generate new samples so as to maximize exposure of the classifier to the attributes-space. Our approach enables deep neural networks to be robust against a wide range of naturally occurring perturbations.
  arXiv  Detail & Related papers  (2020-12-03T10:17:30Z)

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.