AIBullisharXiv – CS AI · 2d ago7/10
🧠Researchers demonstrate that large language models can effectively forecast GPU kernel performance, reducing expensive on-device evaluations during optimization searches. By acting as selective surrogates that know their confidence limits, LLMs enable kernel searches to evaluate multiple candidates under fixed GPU budgets, ultimately discovering faster kernels than baseline approaches.
AIBullisharXiv – CS AI · Apr 157/10
🧠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 · May 276/10
🧠Researchers present U-PINet, a physics-informed neural network that accelerates 3D microwave scattering analysis for radar applications by combining graph-based near-field encoding with hierarchical multi-scale fusion, achieving faster computation than classical solvers while maintaining accuracy on complex geometries.
AIBullisharXiv – CS AI · May 276/10
🧠Researchers introduce Iterative Refinement Neural Operators (IRNO), a method that enhances neural operators by applying learned refinement modules iteratively to correct high-frequency prediction errors. The approach achieves up to 56% error reduction on turbulent flow simulations and demonstrates mathematical convergence guarantees through fixed-point iteration theory.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers propose a novel Ensemble Distributionally Robust Bayesian Optimisation algorithm that addresses context distributional uncertainty in zeroth-order optimization. The method achieves sublinear regret bounds while remaining computationally tractable, improving upon existing state-of-the-art approaches.
AIBullisharXiv – CS AI · Mar 36/103
🧠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
🧠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
🧠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
🧠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.
