Results 61 to 70 of about 2,291 (214)

Data‐driven simulation of crude distillation using Aspen HYSYS and comparative machine learning models

The Canadian Journal of Chemical Engineering, EarlyView.
Integrated Aspen HYSYS–machine learning framework for predicting product yields and quality variables. Abstract Crude oil refining is a complex process requiring precise modelling to optimize yield, quality, and efficiency. This study integrates Aspen HYSYS® simulations with machine learning techniques to develop predictive models for key refinery ...
Aldimiro Paixão Domingos, Heleno Bispo, Thiago Vaz da Costa, Sérgio Mauro da Silva Neiro   +3 more
wiley   +1 more source

AI‐Enabled Precision Dosing in Pediatrics: Enhancing Model‐Informed Decision Making

Clinical Pharmacology &Therapeutics, EarlyView.
Ensuring safe and effective pharmacotherapy for children remains a central challenge in clinical pharmacology, yet rapid advances in AI have not translated into clinical practice. This Perspective highlights how AI‐enabled approaches can enhance model‐informed decision making for precision dosing.
Kei Irie, Tomoyuki Mizuno
wiley   +1 more source

Integral Regularization PINNs for Evolution Equations

Communications in Computational Physics
Evolution equations, including both ordinary differential equations (ODEs) and partial differential equations (PDEs), play a pivotal role in modeling dynamic systems. However, achieving accurate long-time integration for these equations remains a significant challenge.
Xiaodong Feng, Haojiong Shangguan, Tao Tang, Xiaoliang Wan   +3 more
openaire   +2 more sources

Stability Evaluation and Parametric Optimization of Coal‐Concrete Composite Bearing Systems Under Mine‐Water‐Induced Deterioration: Experiments and FEINN Analysis

Energy Science &Engineering, EarlyView.
Mine‐water immersion tests reveal pronounced coal weakening (vs. minor concrete degradation), identifying coal pillars as the stability‐limiting component in composite dams. A coupled FEINN framework quantifies extreme‐pressure stability and ranks multi‐parameter designs via a normalized multi‐indicator scheme, enabling optimized dam configuration for ...
He Wen, Hongchuan Dai, Guixin Zhang, Pengzhe Li, Tong Yang, Cang Gong, Renbo Gao   +6 more
wiley   +1 more source

Space Correlation Constrained Physics Informed Neural Network for Seismic Tomography

Journal of Geophysical Research: Machine Learning and Computation
Physics‐informed neural networks (PINNs) integrate physical constraints with neural architectures and leverage their nonlinear fitting capabilities to solve complex inverse problems.
Yonghao Wang, Zhuo Jia, Bowu Jiang, Wenkai Lu   +3 more
doaj   +1 more source

A Comprehensive Review of AI‐Powered Energy Systems

Energy Science &Engineering, EarlyView.
The role of Artificial Intelligence (AI) in developing next‐generation energy systems is getting more day by day. Therefore, incorporating AI enables real‐time decision‐making and advanced grid management, which are essential for optimizing the use of intermittent renewable sources like wind and solar power.
Armin Razmjoo, Pedram Asef, Masoumeh Javadi, Mousa Marzband, Shady S. Refaat, Muhyaddin Rawa   +5 more
wiley   +1 more source

Data-driven solutions and parameter estimations of a family of higher-order KdV equations based on physics informed neural networks

Scientific Reports
Physics informed neural network (PINN) demonstrates powerful capabilities in solving forward and inverse problems of nonlinear partial differential equations (NLPDEs) through combining data-driven and physical constraints. In this paper, two PINN methods
Jiajun Chen, Jianping Shi, Ao He, Hui Fang   +3 more
doaj   +1 more source

Closing the Loop in Precision Oncology: A Digital Twin‐Driven Paradigm for Dynamic Decision‐Making

iMetaMed, EarlyView.
This review introduces the Closed‐Loop Intelligent Oncology System (CIOS), a five‐layer framework integrating digital twins and AI to enable adaptive, data‐driven cancer treatment. By synthesizing advances in multimodal perception, mechanistic simulation, and safe reinforcement learning, CIOS charts a roadmap toward dynamic, personalized oncology ...
Junye Zhu, Mengqi Yang, Chenxi Ouyang, Lin Zhang   +3 more
wiley   +1 more source

Fourier Shell Analysis: k‐Space‐Based Metrics for Assessing Super‐Resolution in 4D Flow MRI

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To support the emerging field of super‐resolution (SR) in 4D flow MRI by proposing Fourier shell analysis to disentangle resolution enhancement from denoising effects during evaluation. Methods A thoracic aortic 4D flow MRI dataset was synthesized with various degrees of stenosis, providing ground truth flow fields generated using ...
Luuk Jacobs, Pietro Dirix, Sebastian Kozerke   +2 more
wiley   +1 more source

A Physics‐Informed Deep Learning Method With Adaptively Weighted Loss for Modeling Soil Water Flows

Water Resources Research
Richards' equation, widely used to model soil water flows, presents numerical challenges due to the high nonlinearity of its constitutive relationships.
Cunwen Li, Yan Zhu, Xiaoping Zhang, Lili Ju, Qiang Luo, Hui Feng   +5 more
doaj   +1 more source