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Momentum-Conserving Graph Neural Networks for Deformable Objects

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1 SIGNALFIRST DETECTED 30 April 2026
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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

REPORT · WHAT · ArXiv CS.AIOPEN STORY
26 Mar
Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data

arXiv:2603.22380v1 Announce Type: cross Abstract: Data-driven discovery of partial differential equations (PDEs) offers a promising paradigm for uncovering governing physical laws

REPORT · WHAT · ArXiv CS.AIOPEN STORY
16 Sept
Degraded but Not Entirely Ineffective: PE-Based Deformable Graph Neural Networks

arXiv:2609.13712v1 Announce Type: new Abstract: Many real-world scenarios can be represented using graph-structured data. However, traditional GNNs that transmit messages based on

REPORT · HOW · ArXiv CS.AIOPEN STORY
21 Apr
Lightweight Geometric Adaptation for Training Physics-Informed Neural Networks

arXiv:2604.15392v1 Announce Type: cross Abstract: Physics-Informed Neural Networks (PINNs) often suffer from slow convergence, training instability, and reduced accuracy on

REPORT · WHAT · ArXiv CS.AIOPEN STORY
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