Results 161 to 170 of about 118,781 (266)

Algorithmic recourse in sequential decision-making for long-term fairness. [PDF]

Front Big Data
Gumucio F, Zhang L.
europepmc   +1 more source

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

AIChE 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, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short   +4 more
wiley   +1 more source

Beyond Blueprints: Exploring the influence of social science factors on engineers' flood infrastructure design decisions. [PDF]

PLoS One
Ajorlou E, Pourmatin M, Farhadzadeh A, Ghayoomi M, Hewitt E.   +4 more
europepmc   +1 more source

A trust‐region funnel algorithm for gray‐box optimization

AIChE Journal, EarlyView.
Abstract Gray‐box optimization, where parts of optimization problems are represented by algebraic models while others are treated as black‐box models lacking analytic derivatives, remains a challenge. Trust‐region (TR) methods provide a robust framework for gray‐box problems through local reduced models (RMs) for black‐box components, but they are ...
Gul Hameed, Tao Chen, Antonio del Rio Chanona, Lorenz T. Biegler, Michael Short   +4 more
wiley   +1 more source

Enhancing facility maintenance using knowledge-based BIM systems. [PDF]

Sci Rep
Ehab A, Mahdi MA, El-Helloty A.
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

A Framework for Budget-Constrained Zero-Day Cyber Threat Mitigation: A Knowledge-Guided Reinforcement Learning Approach. [PDF]

Sensors (Basel)
Basak M, Shin GY.
europepmc   +1 more source

Graph‐based imitation and reinforcement learning for efficient Benders decomposition

AIChE 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, Zhe Li, Ilias Mitrai, Prodromos Daoutidis   +3 more
wiley   +1 more source

Toward a mechanistic characterisation of marine heatwaves. [PDF]

Sci Rep
Zhao Z, Holbrook NJ, Capotondi A, Cravatte S, Kajtar JB, Gupta AS, Behrens E, Doblin MA, Feng M, Kiss AE, Spillman CM.   +10 more
europepmc   +1 more source

Bayesian Optimization Guiding the Experimental Mapping of the Pareto Front of Mechanical and Flame‐Retardant Properties in Polyamide Nanocomposites

Advanced Intelligent Discovery, EarlyView.
Bayesian optimization enabled the design of PA56 system with just 8 wt% additives, achieving limiting oxygen index 30.5%, tensile strength 80.9 MPa, and UL‐94 V‐0 rating. Without prior knowledge, the algorithm uncovered synergistic effects between aluminum diethyl‐phosphinate and nanoclay.
Burcu Ozdemir, Miguel Hernández‐del‐Valle, Christina Schenk, De‐Yi Wang, Maciej Haranczyk   +4 more
wiley   +1 more source