Results 121 to 130 of about 313,686 (220)

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: quantum computing in agricultural sciences: from theory to reality. [PDF]

J Anim Sci
Tedeschi LO.
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

The random noise modulations on the nonlinear Chiral Schrödinger structures. [PDF]

PLoS One
Alhazmi H, Bajri SA, El-Shewy EK, Abdelrahman MAE.   +3 more
europepmc   +1 more source

Rule based design workshop at the TU Berlin [PDF]

, 2009
Barlieb,, Christophe, Josefsson, Kristoffer, Palz, Norbert, Pfeiffer, Sven, Putz, Ines, Stefanescu,, Dimitrie, Tamke, Martin   +6 more
core  

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

Analytical evaluations using neural network-based method for wave solutions of combined Kairat-II-X differential equation in fluid mechanics. [PDF]

Sci Rep
Zhou P, Manafian J, Lakestani M, Ilhan OA, Bahador NJ, Fattah AA, Mahmoud KH, Nuray R, Nagiyeva N.   +8 more
europepmc   +1 more source

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

Advanced Intelligent Discovery, EarlyView.
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley   +1 more source

Intelligence prediction of integrated circuit reliability based on SSA-LSTM fusion architecture. [PDF]

PLoS One
Qian Y, Liu B, Su B, Zhang C.
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

How large language models need symbolism. [PDF]

Natl Sci Rev
Deng X, Li H.
europepmc   +1 more source