An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case …
M Ramezankhani, A Deodhar, RY Parekh… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep Operator Networks (DeepONets) and their physics-informed variants have shown
significant promise in learning mappings between function spaces of partial differential …
significant promise in learning mappings between function spaces of partial differential …
Interfacial conditioning in physics informed neural networks
Physics informed neural networks (PINNs) have effectively demonstrated the ability to
approximate the solutions of a system of partial differential equations (PDEs) by embedding …
approximate the solutions of a system of partial differential equations (PDEs) by embedding …
Digital twins for optimization of ironmaking operations
V Runkana, S Majumder, VJ Desai, J Arunprasath… - CSI Transactions on …, 2024 - Springer
Manufacturing of steel involves conversion of raw iron ores into different steel products
through a complex network of unit operations. Optimizing manufacturing operations and …
through a complex network of unit operations. Optimizing manufacturing operations and …
An Advanced Physics-Informed Neural Operator for Dynamic, Zero-shot, and Near Real-time Simulation of Aerospace Composite Material Curing Process
M Ramezankhani, A Deodhar, RY Parekh… - AI for Accelerated … - openreview.net
One of the key prerequisites of AI-guided design for manufacturing advanced materials is
the availability of a dynamic, zero-shot and near real-time predictive model for quick and …
the availability of a dynamic, zero-shot and near real-time predictive model for quick and …
[引用][C] Advancements in data center thermal management
R Kalantarpour, K Vafai - 2024 - Elsevier