Results 51 to 60 of about 636 (160)

A Detailed and Comprehensive Account of Fractional Physics‐Informed Neural Networks: From Implementation to Efficiency

Artificial Intelligence for Engineering, EarlyView.
Caputo‐based fPINNs accurately solve fractional ODEs and PDEs while exposing an accuracy–cost trade‐off driven by the history‐dependent fractional derivative. Temporal collocation and shorter time windows are the most effective strategies for improving early‐time accuracy without unnecessary spatial refinement.
Donya Dabiri, Joshua DaRosa, Milad Saadat, Deepak Mangal, Safa Jamali   +4 more
wiley   +1 more source

Control of Accuracy on Taylor-Collocation Method for Load Leveling Problem

Известия Иркутского государственного университета: Серия "Математика", 2019
High penetration of renewable energy sources coupled with decentralization of transport and heating loads in future power systems will result even more complex unit commitment problem solution using energy storage system scheduling for efficient load ...
S. Noeiaghdam, D.N. Sidorov, I.R. Muftahov, A.V. Zhukov   +3 more
doaj   +1 more source

Fault‐Tolerant Fuzzy Boundary Control for Nonlinear Distributed Parameter Systems Under Limited Measurements and Markovian Failures

CAAI Transactions on Intelligence Technology, EarlyView.
ABSTRACT This paper proposes a boundary control method for nonlinear distributed parameter systems (DPSs) with limited boundary measurements (BMs), as typically encountered in networked cyber‐physical processes with spatially distributed dynamics such as thermal and biomedical diffusion systems.
Yanlin Li, Kaibo Shi, Yue Yu, Xiangkun Wang, Ming Zhu, Xiao Cai   +5 more
wiley   +1 more source

The Influence of Random Element Displacement on DOA Estimates Obtained with (Khatri–Rao-)Root-MUSIC

Sensors, 2014
Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the ...
Veronique Inghelbrecht, Jo Verhaevert, Tanja van Hecke, Hendrik Rogier   +3 more
doaj   +1 more source

Predicting disease spread from host movement data: Chronic wasting disease in North America as a case study

Journal of Animal Ecology, EarlyView.
This paper estimates the rate of chronic wasting disease spread in multiple regions and compares these rates with model predictions based on deer movement data. Abstract Rare long‐distance movements can increase the spatial spread of invasive species and shifts in species ranges. For wildlife disease spread, however, seasonal migrations may only matter
Paul C. Cross, Blake Lowrey, Matthew Kauffman, Evelyn Merrill   +3 more
wiley   +1 more source

Walsh function-based numerical approach for nonlinear stochastic integral equations: Application to stochastic logistic models

Boundary Value Problems
The current research study proposes an efficient numerical method for obtaining an approximate solution to nonlinear stochastic integral equations implementing the collocation method and the Walsh operational matrices.
Prit Pritam Paikaray, Sanghamitra Beuria, Nigam Chandra Parida, Mohammad Izadi   +3 more
doaj   +1 more source

Genomic Evolution and Immune Contexture With Therapeutic Relevance in Pancreatic Neuroendocrine Neoplasms

Cancer Science, EarlyView.
Lineage‐dependent immunogenomic landscapes and biologically informed therapy in pancreatic neuroendocrine neoplasms. Pancreatic neuroendocrine neoplasms display lineage‐dependent immunogenomic landscapes, in which genomic alterations, epigenetic states, antigen‐presentation status, immune‐cell infiltration, and suppressive microenvironments co‐evolve ...
Yohei Tabe, Eito Nakagawa, Daisuke Asano, Eriko Katsuta, Daisuke Ban, Shogo Kumagai   +5 more
wiley   +1 more source

A spectral collocation method for stochastic Volterra integro-differential equations and its error analysis

Advances in Difference Equations, 2019
Volterra integro-differential equations arise in the modeling of natural systems where the past influence the present and future, for example pollution, population growth, mechanical systems and financial market. Furthermore, as many real-world phenomena
Sami Ullah Khan, Mushtaq Ali, Ishtiaq Ali   +2 more
doaj   +1 more source

L‐VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts

Computer Graphics Forum, EarlyView.
L‐VISP is a human‐machine solution that uses visual analytics for LSTM modelling in clinical research. L‐VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context.
C. Floricel, Y. Wang, A. Wentzel, C. D. Fuller, G. E. Marai, M. E. Papka, G. Canahuate   +6 more
wiley   +1 more source

Visual Ensemble Analysis With Deep Learning Prediction for Studying the Effect of Tissue Properties on Radiofrequency Ablation

Computer Graphics Forum, EarlyView.
We present an interactive visual analysis tool to study how patient‐specific tissue properties influence radiofrequency ablation outcomes. Using a deep‐learning surrogate model, we predict ablation volumes for unseen parameter settings with accuracy superior to interpolation, supporting improved treatment planning. Abstract Radiofrequency (RF) ablation
R. Sabbagh Gol, M. Evers, K. Heimes, T. Gerrits, S. Gyawali, D. Sinden, T. Preusser, L. Linsen   +7 more
wiley   +1 more source