#surrogate-models News & Analysis

5 articles tagged with #surrogate-models. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · 4d ago7/10

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AutoSurrogate: An LLM-Driven Multi-Agent Framework for Autonomous Construction of Deep Learning Surrogate Models in Subsurface Flow

AutoSurrogate is an LLM-driven framework that automates the construction of deep learning surrogate models for subsurface flow simulation, enabling domain scientists without machine learning expertise to build high-quality models through natural language instructions. The system autonomously handles data profiling, architecture selection, hyperparameter optimization, and quality assessment while managing failure modes, demonstrating superior performance to expert-designed baselines on geological carbon storage tasks.

AIBullisharXiv – CS AI · Mar 36/103

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Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport

Researchers introduce Hyperparameter Trajectory Inference (HTI), a method to predict how neural networks behave with different hyperparameter settings without expensive retraining. The approach uses conditional Lagrangian optimal transport to create surrogate models that approximate neural network outputs across various hyperparameter configurations.

AINeutralarXiv – CS AI · Mar 174/10

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Informative Perturbation Selection for Uncertainty-Aware Post-hoc Explanations

Researchers introduce EAGLE, a new framework for explaining black-box machine learning models using information-theoretic active learning to select optimal data perturbations. The method produces feature importance scores with uncertainty estimates and demonstrates improved explanation reproducibility and stability compared to existing approaches like LIME.

AINeutralarXiv – CS AI · Feb 274/105

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Learning geometry-dependent lead-field operators for forward ECG modeling

Researchers developed a new AI-powered surrogate model for ECG simulations that combines geometry encoding with neural networks to predict lead-field gradients. The method achieves high accuracy (5° mean angular error, <2.5% relative error) while reducing computational costs and data requirements compared to traditional full-order models.

AINeutralarXiv – CS AI · Mar 34/105

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Adaptive Uncertainty-Guided Surrogates for Efficient phase field Modeling of Dendritic Solidification

Researchers developed a new AI-powered surrogate model using XGBoost and CNNs to significantly reduce computational costs in phase field simulations for metal solidification processes. The adaptive uncertainty-guided approach achieves accurate predictions while requiring fewer expensive simulations and reducing CO2 emissions in additive manufacturing applications.