Results 181 to 190 of about 118,655 (315)

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

Blood lipidome profiling reveals potential biomarkers linked to health and carcass quality traits in pigs. [PDF]

open access: yesGenet Sel Evol
Hernández-Banqué C   +6 more
europepmc   +1 more source

Free will and determinism as a function of schizotypy and religiosity [PDF]

open access: yes, 2014
Armengol, J.   +5 more
core  

AI in chemical engineering: From promise to practice

open access: yesAIChE 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   +4 more
wiley   +1 more source

Technoeconomic and sustainability analysis of batch and continuous crystallization for pharmaceutical manufacturing

open access: yesAIChE Journal, EarlyView.
Abstract In pharmaceutical industries, continuous manufacturing methods have already been well established to improve productivity and process intensification. However, to better understand the trade‐offs of continuous crystallizers over the existing batch production systems, a robust technoeconomic cost and sustainability analysis is necessary to ...
Jungsoo Rhim, Zoltan K. Nagy
wiley   +1 more source

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

open access: yesAdvanced 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

Exploring Ohm's Law: The Randomness of Determinism. [PDF]

open access: yesEntropy (Basel)
Cuadras A   +2 more
europepmc   +1 more source

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