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The paper proposes a dynamic aggregation enhanced efficient global optimization algorithm (DA-EGO) for solving high-dimensional turbomachinery design problems, validated through benchmark tests and aerodynamic applications.
The paper proposes a surrogate model approach to efficiently estimate inconsistency response surfaces in Cyber-Physical Systems under uncertainty, enabling scalability for sensitivity analysis and consistency repair.
This paper introduces a physics-aware autoencoder-based latent-space framework for reduced-order forward modeling and variational parameter estimation in parametric dynamical systems, demonstrated on computational fluid dynamics benchmarks. The method enables differentiable surrogate-based inverse modeling and shows improved calibration robustness under realistic noisy or partial observations.
This paper presents a multimodal auto-regressive transformer surrogate that models variable well operations and geological uncertainty for geological carbon storage, achieving accurate predictions and enabling uncertainty quantification via MCMC data assimilation.
This paper proposes a model-agnostic, post-hoc interpretation framework for black-box LLMs using sentence-level energy landscapes. A surrogate Energy-Based Model simulates the target LLM, and a lightweight interpreter network identifies which prompt sentences most influence a given output, without requiring further API calls.
Introduces HyperODE, a zero-shot surrogate that maps ODE structures to hypergraphs, enabling simulation and parameter inference across entire families of dynamical systems without retraining.
This paper introduces PCINN, a physics-chemistry-informed neural network that acts as a hybrid AI surrogate for real-time spatial atomic layer deposition (SALD) coverage prediction, achieving CFD-level accuracy in ~77ms and enabling reliable kinetics inversion via identifiability analysis.
This paper proposes HERO (History-Enriched Rollout Training), a method that augments standard trajectory supervision for autoregressive neural operators with relative supervision from the model's own optimization history, improving long-horizon accuracy and stability on PDE benchmarks.
This paper evaluates recursive weight-sharing transformer architectures as parameter-efficient surrogate models for semiconductor thermo-mechanical reliability prediction, comparing performance, parameter count, and computational cost on small engineering datasets.
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.