Results 151 to 160 of about 289,279 (277)

A multiscale Bayesian optimization framework for process and material codesign

AIChE Journal, EarlyView.
Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley   +1 more source

Iterative design of a NAND hybrid riboswitch by deep batch Bayesian optimization. [PDF]

Nucleic Acids Res
Kelvin D, Kubaczka E, Karava M, Koeppl H, Suess B.   +4 more
europepmc   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

AIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi, Tomoyuki Yajima, Yoshiaki Kawajiri   +2 more
wiley   +1 more source

Epidemiological model calibration via graybox Bayesian optimization. [PDF]

Infect Dis Model
Niu P, Yoon BJ, Qian X.
europepmc   +1 more source

AI in chemical engineering: From promise to practice

AIChE Journal, EarlyView.
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew, Ronnie Andersson, Thomas Bierweiler, Patrik Ryttestal, Torsten Wik   +4 more
wiley   +1 more source

Bayesian Optimization for Multicomponent Supramolecular Systems. [PDF]

J Am Chem Soc
Jansen SAH, Markvoort AJ, Graaf FV, Rutten MGTA, Dankers PYW, Vantomme G, de Greef TFA, Meijer EW.   +7 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Navigating Ternary Doping in Li-ion Cathodes With Closed-Loop Multi-Objective Bayesian Optimization. [PDF]

Adv Mater
Zeinali Galabi N, Liu CH, Jain M, Kamel M, Jia S, Bengio Y, McCalla E.   +6 more
europepmc   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Advanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour, Sara Soltanabadi Farshi, Tuanna Ozcan, Amir Mohammad Ketabchi, Alper Kiraz, Savas Tasoglu   +5 more
wiley   +1 more source

Predicting the Losses of Marine Aquaculture Equipment Due to Disasters Along the Fujian Coastline Using a Bayesian-Optimized SVR Model

Haiyang Kaifa yu guanli
To predict the economic losses caused by disasters affecting marine aquaculture equipment and to ensure the healthy development of the marine aquaculture industry, this study analyzes and forecasts the losses of marine aquaculture equipment in the ...
LI Xurui, DONG Dibo, GUO Qiaoying
doaj