Results 71 to 80 of about 2,291 (214)

Optimal control of interactions between invasive alien and native species in a certain time period with the r-PINN approach

Results in Control and Optimization
The spread of invasive species poses a significant challenge to native biodiversity and ecosystem stability. An optimal control strategies to minimize the negative impacts of invasive species populations on native species and the ecosystem must be done ...
Yudi Ari Adi, Danang A. Pratama, Maharani A. Bakar, Sugiyarto Surono, Suparman, Agung Budiantoro   +5 more
doaj   +1 more source

On the Question at the End of Theodicy

Religions, 2017
This article argues that theodicy provides an insufficient response to suffering - one that often further victimizes those who suffering most. In it’s place, I argue for a moralist response based on Albert Camus and W. E. B. Du Bois.
Anthony B. Pinn
doaj   +1 more source

Nordgren PINNs to VQE: Advancing Hydraulic Fracturing Simulations in Shale Reservoirs

International Journal for Numerical and Analytical Methods in Geomechanics, EarlyView.
ABSTRACT This study advances hydraulic fracturing simulations in shale reservoirs using two computational paradigms, Physics‐Informed Neural Networks (PINNs) and the Variational Quantum Eigensolver (VQE). PINNs were employed to solve Nordgren's equation, which governs fracture width evolution, by embedding physical laws into the neural network ...
Dennis Delali Kwesi Wayo, Sonny Irawan, Mohd Zulkifli Bin Mohamad Noor, Mudasar Zafar, Shynar Juziyeva, Camila Martins Saporetti, Leonardo Goliatt, Randy Hazlett   +7 more
wiley   +1 more source

NDAWL-PINN: a new non-dimensionalization and multi-task learning approach for efficient training of physics-informed neural networks to solve the shallow water equations

Engineering Applications of Computational Fluid Mechanics
The exploration of deep learning methodologies has recently generated significant interest in the use of Physics-Informed Neural Networks (PINNs) to address complex physical problems governed by partial differential equations (PDEs).
Xin Qi, Dawei Zhang, Fan Wang, Wuxia Bi, Mingda Lu   +4 more
doaj   +1 more source

Physics‐Informed Neural Networks for Battery Degradation Prediction Under Random Walk Operations

Quality and Reliability Engineering International, EarlyView.
ABSTRACT This study addresses the challenge of predicting the state of health (SoH) and capacity degradation in Battery Energy Storage Systems (BESS) under highly variable conditions induced by frequent control adjustments. In environments where random walk behavior prevails due to stochastic control commands, conventional estimation methods often ...
Alaa Selim, Huadong Mo, Hemanshu Pota, Daoyi Dong   +3 more
wiley   +1 more source

An Improved Causal Physics-Informed Neural Network Solution of the One-Dimensional Cahn–Hilliard Equation

Applied Sciences
Physics-Informed Neural Networks (PINNs) provide a promising framework for solving partial differential equations (PDEs). By incorporating temporal causality, Causal PINN improves training stability in time-dependent problems.
Jinyu Hu, Jun-Jie Huang
doaj   +1 more source

Comparison of Trefftz-Based PINNs and Standard PINNs Focusing on Structure Preservation

CoRR
In this study, we investigate the capability of physics-informed neural networks (PINNs) to preserve global physical structures by comparing standard PINNs with a Trefftz-based PINN (Trefftz-PINN). The target problem is the reproduction of mag-netic field-line structures in a helical fusion reactor configuration.
openaire   +2 more sources

PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling

CoRR
Recent advances in Scientific Machine Learning have shown that second-order methods can enhance the training of Physics-Informed Neural Networks (PINNs), making them a suitable alternative to traditional numerical methods for Partial Differential Equations (PDEs).
Andrea Bonfanti, Ismael Medina, Roman List, Björn Staeves, Roberto Santana 0001, Marco Ellero   +5 more
openaire   +2 more sources

Artificial Intelligence for Blue Transformation: A Review of Predictive Modeling and Decision Support Systems in Sustainable Aquaculture

Sustainable Development, EarlyView.
ABSTRACT The FAO's “blue transformation” roadmap necessitates a fundamental shift towards precision aquaculture to meet global food security targets while minimizing environmental footprints. This review provides a comprehensive overview of how artificial intelligence (AI) and decision support systems (DSS) serve as pivotal enablers for the “better ...
Mustafa Öz, Enes Üstüner
wiley   +1 more source

PINNs for Medical Image Analysis: A Survey

CoRR
The incorporation of physical information in machine learning frameworks is transforming medical image analysis (MIA). By integrating fundamental knowledge and governing physical laws, these models achieve enhanced robustness and interpretability.
Chayan Banerjee, Kien Nguyen Thanh, Olivier Salvado, Truyen Tran 0001, Clinton Fookes   +4 more
openaire   +2 more sources