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Pure and Physics-Guided Deep Learning Solutions for Spatio-Temporal Groundwater

BACKGROUND
2 SIGNALSFIRST DETECTED 30 March 2026
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18 Sept
30 Mar
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Other feeds and industry voices
15 Jun
Korzhinskii-Net: Physics-Informed Neural Network for Sub-Surface Mineral Prospectivity Modelling

arXiv:2606.13695v1 Announce Type: cross Abstract: Mineral prospectivity modelling (MPM) underpins exploration economics, yet most operational pipelines reduce to data-driven

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7 May
A Physics-Aware Framework for Short-Term GPU Power Forecasting of AI Data Centers

arXiv:2605.04074v1 Announce Type: cross Abstract: AI data centers experience rapid fluctuations in power demand due to the heterogeneity of computational tasks that they have to

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25 Aug
Physics-Knowledge-Guided Hybrid Neural Learning for Arctic Sea Ice Concentration Evolution and Short-Range Prediction

arXiv:2608.21767v1 Announce Type: new Abstract: Accurate modeling of sea ice concentration (SIC) evolution is essential for polar climate assessment and short?range sea ice

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26 Mar
UniFluids: Unified Neural Operator Learning with Conditional Flow-matching

arXiv:2603.22309v1 Announce Type: cross Abstract: Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of

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26 Mar
Sparsely-Supervised Data Assimilation via Physics-Informed Schr\"odinger Bridge

arXiv:2603.22319v1 Announce Type: cross Abstract: Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal

REPORT · HOW · ArXiv CS.AIOPEN STORY
1 May
Compositional Meta-Learning for Mitigating Task Heterogeneity in Physics-Informed Neural Networks

arXiv:2604.26999v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) approximate solutions of partial differential equations (PDEs) by embedding physical laws

REPORT · WHAT · ArXiv CS.AIOPEN STORY
14 Aug
Physics-Informed Implicit Neural Representations for Improved Myocardial Perfusion MRI Quantification

arXiv:2608.11282v1 Announce Type: cross Abstract: Quantifying myocardial perfusion from cardiac magnetic resonance (CMR) can be achieved by fitting tracer-kinetic models to the

REPORT · WHAT · ArXiv CS.AIOPEN STORY
20 Oct
$M^2Net$: Unifying Scale-Specific Structures and Prototype Decomposition for Spatial-Temporal Forecasting

arXiv:2610.24516v1 Announce Type: new Abstract: Spatial-temporal forecasting is pivotal for intelligent urban management yet remains challenging due to the intricate coupling of

REPORT · WHERE · ArXiv CS.AIOPEN STORY
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