#surrogate modeling

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19 papers match

cond-mat.mtrl-sci2026

Deep Learning for Accelerated Long-Horizon Forecasting of Multicomponent Multiphase Microstructure Evolution in High-Entropy Alloys

Hamidreza Razavi, Nele Moelans

The paper introduces a surrogate model combining autoencoders, graph convolutional networks, and LSTM to rapidly predict long‑term microstructure evolution in multicomponent high‑e…

#phase-field modeling#high-entropy alloys#graph neural networks#autoencoder
cs.LG2026

Oracle-Budgeted Molecular Optimization with Short-Term Graph Memory

Jiannan Yang, Veronika Thost, Xiang Ling +1

The paper proposes a short-term graph memory module that uses an online graph neural surrogate to pre‑screen candidate molecules, allowing a fixed oracle budget to be spent on high…

#molecular optimization#oracle budget#graph neural networks#surrogate modeling
cs.LG2026

Learning features from Newton's algorithm: a way to accelerate nonlinear parametrized PDE solvers

Rémy Vallot, Florian de Vuyst, Thibault Dairay +1

The paper introduces a two‑stage method that learns features from precomputed Newton trajectories to predict a surrogate solution and then apply a cheap corrective step, providing…

#newton's method#reduced-order modeling#parameterized pdes#machine learning
cs.LG2026

Surrogate assisted diversity estimation in neural ensemble search

Alexandr Udeneev, Petr Babkin, Oleg Bakhteev

The paper proposes a dual‑objective surrogate‑guided method for neural ensemble search that predicts both accuracy and diversity of candidate architectures, enabling efficient cons…

#neural architecture search#ensemble learning#surrogate modeling#diversity estimation
cs.AI2026

AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining

Jingyang Yi, Jian Yang, Yifei Jin +2

AlphaSchema is a framework that builds a structured semantic space for trading factor generation, letting large language models explore and implement candidate factors separately w…

#alpha mining#large language models#semantic search#financial factor generation
eess.SY2026

From Crop and Energy Data to Optimized Lighting Scheduling: A Surrogate-Based MILP Framework for Vertical Farming

Francesco Ceccanti, Andrea Baccioli, Aldo Bischi

The paper presents a surrogate-based mixed‑integer linear programming framework to schedule artificial lighting in hydroponic vertical farms, reducing electricity use and cost whil…

#vertical farming#lighting optimization#surrogate modeling#mixed-integer linear programming