Results 161 to 170 of about 318,704 (275)

Discrete-time Mittag-Leffler state estimation for fractional-order quaternion memristive neural networks. [PDF]

Sci Rep
Huang Q, Tu Z.
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

Integration field-based breadth-first search for flow field pathfinding. [PDF]

Sci Rep
Yang J, Chen X, Dong M.
europepmc   +1 more source

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Advanced Intelligent Discovery, EarlyView.
A multimodal fusion pipeline predicts high‐resolution ion distributions in imaging mass spectrometry by integrating Fourier transform ion cyclotron resonance, time‐of‐flight matrix‐assisted laser desorption/ionization, and time‐of‐flight secondary ion mass spectrometry data.
Md Inzamam Ul Haque, Ramakrishnan Kannan, Jacob D. Hinkle, Sylwia A. Stopka, Anton V. Ievlev, Nathalie Y. R. Agar, Olga S. Ovchinnikova, Debangshu Mukherjee   +7 more
wiley   +1 more source

Mutual information-based hierarchical NBV decision for active semantic visual SLAM under dynamic environments. [PDF]

Sci Rep
Yang Z, Sang AWY, Muthugala MAVJ, Elara MR.   +3 more
europepmc   +1 more source

Semiregular Degenerate Refinement for 3D Discrete Global Grid Systems

, 2020
Benjamin Ulmer
openalex   +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

Analytical and numerical properties of an extended angiogenesis PDEs model. [PDF]

J Math Biol
De Luca P, Marcellino L.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

Global distribution and changes of leaf-level intrinsic water use efficiency and their responses to water stress. [PDF]

Nat Commun
Wang X, Fu Z, Ciais P, Wang L, Buchmann N, Keenan TF, De Kauwe M, Peñuelas J, Chen G, Gong X, Xiao J, Li X, Xie Q, Stoy PC, Makowski D, Smith WK, Wang H, Wang S, Zhang F, Niu S.   +19 more
europepmc   +1 more source