Results 111 to 120 of about 56,420 (266)

Traditional or adaptive design of experiments? A pilot-scale comparison on wood delignification

Heliyon
Traditional design of experiments and response surface methodology are widely used in engineering and process development. Bayesian optimization is an alternative machine learning approach that adaptively selects successive experimental conditions based ...
Hannu Rummukainen, Hanna Hörhammer, Pirkko Kuusela, Jorma Kilpi, Jari Sirviö, Mikko Mäkelä   +5 more
doaj   +1 more source

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

An open-source parallel algorithm of Bayesian-based global search with Hooke–Jeeves local refinement for multi-objective optimization problems

Nonlinear Analysis
Contemporary engineering and scientific problems often involve computationally intensive optimization tasks. This paper proposes a parallel version of the hybrid algorithm of the previously proposed Bayesian-based global search with Hooke–Jeeves local ...
Linas Litvinas
doaj   +1 more source

Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers

Advanced Science, EarlyView.
FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Prompt Optimization in Large Language Models

Mathematics
Prompt optimization is a crucial task for improving the performance of large language models for downstream tasks. In this paper, a prompt is a sequence of n-grams selected from a vocabulary.
Antonio Sabbatella, Andrea Ponti, Ilaria Giordani, Antonio Candelieri, Francesco Archetti   +4 more
doaj   +1 more source

Bilevel Optimization by Conditional Bayesian Optimization

Bilevel optimization problems have two decision-makers: a leader and a follower (sometimes more than one of either, or both). The leader must solve a constrained optimization problem in which some decisions are made by the follower. These problems are much harder to solve than those with a single decision-maker, and efficient optimal algorithms are ...
Vedat Dogan, Steven D. Prestwich
openaire   +2 more sources

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Model-output-based federated Bayesian optimization

Complex & Intelligent Systems
Bayesian optimization (BO) has evolved from single-agent optimization to multi-agent collaborative optimization, namely Federated Bayesian Optimization (FBO), aimed at collaboratively improving the optimization performance of all agents.
Lin Yang, Qiqi Liu, Zhigang Zhao, Yunhe Wang, Junhua Gu   +4 more
doaj   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Generation of Probabilistic Bits by Exploiting Orthogonal Spin Currents in Magnetic Trilayers

Advanced Science, EarlyView.
Fe/Ti/CoFeB trilayers generate orthogonal spin currents that drive stochastic spin–orbit‐torque switching for probabilistic‐bit operation. The switching probability is continuously controlled by the in‐plane magnetic field and drive current, enabling tunable random bit generation.
Donghyeon Han, Chaehyeon Shin, Seok‐Jong Kim, Yunho Jang, Daekyu Koh, Taehwan Kim, Jaeheon Jung, Geon‐Woo Baek, Minseok Kang, Eunseok Kim, Geun‐Hee Lee, Jeongchun Ryu, Makoto Kohda, Junsaku Nitta, Kab‐Jin Kim, Jongsun Park, Kyung‐Jin Lee, Byong‐Guk Park   +17 more
wiley   +1 more source