Abstract: Bayesian optimization is commonly used to optimize black-box functions associated with simulations in engineering and science. Bayesian optimization contains two essential components: the ...

Supplementary materials for Siggraph 2020 technical paper Fabrication-in-the-Loop Co-Optimization of Surfaces and Styli for Drawing Haptics Data-driven surrogate model of the Huxley muscle model based ...

Data-driven surrogate model of the Huxley muscle model based on Gated Recurrent Unit (GRU), Temporal Convolutional Network (TCN), Nested Long Short-Term Unit (Nested LSTM).. Surrogate model for ...

McKetta Department of Chemical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States Oden Institute for Computational Engineering and Sciences, The University of Texas at ...

1 Faculty of Land Resources Engineering, Kunming University of Science and Technology Kunming, Kunming, China 2 Pangang Group Mining Company Limited Panzhihua, Panzhihua, Sichuan, China The stability ...

Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russian Federation Academic University, Russian Academy of Sciences, St. Petersburg 194021, Russian Federation ...

Abstract: In early phases of electric vehicle development, powertrain design requires a system-level approach with sufficiently accurate component models. This paper presents optimization frameworks ...

ABSTRACT: This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian Generalized Additive Mixed Models (GAMMs).

1 State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu, China 2 State Key Laboratory of Mountain Bridge and Tunnel Engineering, ...

The AI industry stands at an inflection point. While the previous era pursued larger models—GPT-3's 175 billion parameters to PaLM's 540 billion—focus has shifted toward efficiency and economic ...