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Introduces a deep learning surrogate pipeline based on Swin3D Transformer to predict spatiotemporal discharge dynamics in lithium-ion batteries, significantly reducing computational cost while improving accuracy over point cloud baselines.
This paper presents a novel approach combining Graph Neural Networks with augmented Neural Ordinary Differential Equations (GNODE) for stable and accurate spatio-temporal prediction of unsteady airfoil aerodynamics, outperforming autoregressive baselines on transonic shock and non-linear dynamics tests.
This paper proposes a dual-domain fused LSTM (DDF-LSTM) model for time-dependent reliability analysis that integrates time-independent random variables and stochastic processes to efficiently estimate failure probabilities via Monte Carlo simulation.
This paper presents a machine learning surrogate model to predict component criticality in interdependent power and communication networks, achieving high correlation with a high-fidelity simulator while being computationally efficient.
This paper presents an advanced GNN surrogate for forecasting CO2 plume migration in complex geological formations, introducing an anisotropic message-passing mechanism to handle directional transport, aiming to accelerate carbon capture and storage simulations.
StampFormer is a physics-guided deep learning framework that fuses geometry and material properties to predict FEA outcomes for sheet metal stamping in under a second, achieving high fidelity with less than 8.5% relative error.