Results 181 to 190 of about 380,569 (245)

A multiscale Bayesian optimization framework for process and material codesign

open access: yesAIChE 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

Trust‐region filter algorithms utilizing Hessian information for gray‐box optimization

open access: yesAIChE Journal, EarlyView.
Abstract Optimizing industrial processes often involves gray‐box models that couple algebraic glass‐box equations with black‐box components lacking analytic derivatives. Such systems challenge derivative‐based solvers. The classical trust‐region filter (TRF) algorithm provides a robust framework but requires extensive parameter tuning and numerous ...
Gul Hameed   +4 more
wiley   +1 more source

A rational strategy to optimize photodrugs by Molecular Dynamics simulations for killing drug‐resistant Gram‐negative bacteria

open access: yesAIChE Journal, EarlyView.
Abstract Antimicrobial resistance has become a major threat to human health, particularly for Gram‐negative bacteria such as Acinetobacter baumannii and Klebsiella pneumoniae. Disruption of membrane integrity is regarded as a promising antimicrobial strategy that does not induce distinct drug resistance, while increasing the internalization of drug ...
Lei Hao   +10 more
wiley   +1 more source

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

open access: yesAIChE 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   +2 more
wiley   +1 more source

Graph‐based imitation and reinforcement learning for efficient Benders decomposition

open access: yesAIChE Journal, EarlyView.
Abstract This work introduces an end‐to‐end graph‐based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network, which takes as input a bipartite graph representation of the master problem and proposes a candidate solution.
Bernard T. Agyeman   +3 more
wiley   +1 more source

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