DS-HGNN achieves lower RMSE for stress and displacement prediction on stiffened panels than six benchmark GNN models and matches top accuracy with 19-38% fewer training samples.
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arXiv preprint arXiv:2010.03409 (2020)
25 Pith papers cite this work, alongside 49 external citations. Polarity classification is still indexing.
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abstract
Mesh-based simulations are central to modeling complex physical systems in many disciplines across science and engineering. Mesh representations support powerful numerical integration methods and their resolution can be adapted to strike favorable trade-offs between accuracy and efficiency. However, high-dimensional scientific simulations are very expensive to run, and solvers and parameters must often be tuned individually to each system studied. Here we introduce MeshGraphNets, a framework for learning mesh-based simulations using graph neural networks. Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation. Our results show it can accurately predict the dynamics of a wide range of physical systems, including aerodynamics, structural mechanics, and cloth. The model's adaptivity supports learning resolution-independent dynamics and can scale to more complex state spaces at test time. Our method is also highly efficient, running 1-2 orders of magnitude faster than the simulation on which it is trained. Our approach broadens the range of problems on which neural network simulators can operate and promises to improve the efficiency of complex, scientific modeling tasks.
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Fine-tuning neural PDE operators to regime endpoints reveals a physical direction in weight space that CCM uses to compose accurate merged models for new or extrapolated regimes from metadata or short prefixes.
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
A lightweight DNS-derived dataset of deformable bubble swarms with spherical-harmonic shape descriptors enables data-driven emulators that achieve 10^4-10^5x speedup over direct simulation.
A tri-branch diffusion model co-generates RGB, depth, and optical flow from a single RGB-D image, and an inverse dynamics head on its internal latents achieves state-of-the-art bimanual manipulation success rates.
NEXUS introduces a graph-based neural energy-field model that derives forces from scalar energy and dissipation terms to achieve physically consistent contact-rich 3D dynamics.
PEACH uses a novel spatio-temporal point cloud sequence encoder plus auxiliary supervision to enable zero-shot adaptation of graph network simulators to unseen physical properties, outperforming mesh-based baselines in simulation accuracy while being more deployable for real scenes.
Mask-Morph Graph U-Net morphs coarse graph hierarchies with barycentric parameterization and applies masked supervised pretraining to improve generalizability of hierarchical GNN surrogates for crashworthiness prediction on variable meshes.
U-HNO uses adaptive per-point routing in a U-shaped hybrid architecture to achieve state-of-the-art accuracy on PDE benchmarks with sharp localized features.
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GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
Instrumented data augments observations with mechanistic models, uncertainty, and counterfactuals to enable causal interventions via Pearl's do-operator in scientific machine learning.
Embodied AI requires query-conditioned world models that select the simplest physical abstraction sufficient to answer intervention queries.
LEIA is a world model for autoregressive 3D simulation of architected materials under interactive loading, benchmarked on MicroPlate and applied to surrogate-guided de novo design search with finite-element validation.
A GNN-based hybrid twin learns the ignorance component of physics simulations from sparse data and generalizes corrections across meshes, geometries, and loads in nonlinear heat transfer.
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A GNN surrogate model for HTS magnet circuits achieves 4.3% mean MAPE on voltage prediction within the tested design space and supports zero-shot and few-shot generalization to new topologies.
Mesh graph network generalizes von Mises stress prediction to unseen 2D plate geometries and loads, outperforming conventional ML models on held-out cases.
A CNN surrogate trained on high-fidelity simulations is embedded in MCMC to infer multiphase flow parameters from full-field FluidFlower experiment observations, yielding better simulation-experiment agreement than manual calibration.
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