Imagen de Portada

Computational Fluid Dynamics Optimization of F1 Front Wing using Physics Informed Neural Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Shah, Naval
Formato:	Preprint
Publicado:	2025
Materias:	Computational Engineering, Finance, and Science Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Physics-Informed Neural Networks for Shell Structures
por: Bastek, Jan-Hendrik, et al.
Publicado: (2022)

Efficient Aircraft Design Optimization Using Multi-Fidelity Models and Multi-fidelity Physics Informed Neural Networks
por: Sarker, Apurba
Publicado: (2024)

PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks
por: Zhao, Zhiyuan, et al.
Publicado: (2023)

Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
por: Wong, Jian Cheng, et al.
Publicado: (2025)

Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect
por: Wong, Jian Cheng, et al.
Publicado: (2023)

PIVONet: A Physically-Informed Variational Neuro ODE Model for Efficient Advection-Diffusion Fluid Simulation
por: Cheung, Hei Shing, et al.
Publicado: (2026)

Feature Mapping in Physics-Informed Neural Networks (PINNs)
por: Zeng, Chengxi, et al.
Publicado: (2024)

Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems
por: Sharma, Vinay, et al.
Publicado: (2025)

Multi-Grid Graph Neural Networks with Self-Attention for Computational Mechanics
por: Garnier, Paul, et al.
Publicado: (2024)

Multi-level datasets training method in Physics-Informed Neural Networks
por: Tsai, Yao-Hsuan, et al.
Publicado: (2025)

Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
por: Yang, Sunwoong, et al.
Publicado: (2024)

Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting
por: Immordino, Gabriele, et al.
Publicado: (2024)

Physics-Informed Diffusion Models
por: Bastek, Jan-Hendrik, et al.
Publicado: (2024)

Learning Fluid-Structure Interaction with Physics-Informed Machine Learning and Immersed Boundary Methods
por: Farea, Afrah, et al.
Publicado: (2025)

WindsorML: High-Fidelity Computational Fluid Dynamics Dataset For Automotive Aerodynamics
por: Ashton, Neil, et al.
Publicado: (2024)

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
por: Wasi, Azmine Toushik, et al.
Publicado: (2024)

FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamics
por: Li, Pingzhi, et al.
Publicado: (2026)

Neural Persistence Dynamics
por: Zeng, Sebastian, et al.
Publicado: (2024)

Improving hp-Variational Physics-Informed Neural Networks for Steady-State Convection-Dominated Problems
por: Anandh, Thivin, et al.
Publicado: (2024)

Finite-PINN: A Physics-Informed Neural Network with Finite Geometric Encoding for Solid Mechanics
por: Li, Haolin, et al.
Publicado: (2024)

Form-Finding and Physical Property Predictions of Tensegrity Structures Using Deep Neural Networks
por: Chen, Muhao, et al.
Publicado: (2024)

The Enhanced Physics-Informed Kolmogorov-Arnold Networks: Applications of Newton's Laws in Financial Deep Reinforcement Learning (RL) Algorithms
por: Thoi, Trang, et al.
Publicado: (2026)

Physics-Informed Machine Learning for Smart Additive Manufacturing
por: Sharma, Rahul, et al.
Publicado: (2024)

Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model
por: Etienam, Clement, et al.
Publicado: (2024)

Optimizing Performance of Feedforward and Convolutional Neural Networks through Dynamic Activation Functions
por: Rane, Chinmay, et al.
Publicado: (2023)

Equi-Euler GraphNet: An Equivariant, Temporal-Dynamics Informed Graph Neural Network for Dual Force and Trajectory Prediction in Multi-Body Systems
por: Sharma, Vinay, et al.
Publicado: (2025)

Physics-Informed Machine Learning for Grade Prediction in Froth Flotation
por: Nasiri, Mahdi, et al.
Publicado: (2024)

DrivAerML: High-Fidelity Computational Fluid Dynamics Dataset for Road-Car External Aerodynamics
por: Ashton, Neil, et al.
Publicado: (2024)

Adaptive Collocation Point Strategies For Physics Informed Neural Networks via the QR Discrete Empirical Interpolation Method
por: Celaya, Adrian, et al.
Publicado: (2025)

Temperature Distribution Prediction in Laser Powder Bed Fusion using Transferable and Scalable Graph Neural Networks
por: Raut, Riddhiman, et al.
Publicado: (2024)

Neural Modular Physics for Elastic Simulation
por: Li, Yifei, et al.
Publicado: (2025)

Multi-Objective Loss Balancing in Physics-Informed Neural Networks for Fluid Flow Applications
por: Farea, Afrah, et al.
Publicado: (2025)

AhmedML: High-Fidelity Computational Fluid Dynamics Dataset for Incompressible, Low-Speed Bluff Body Aerodynamics
por: Ashton, Neil, et al.
Publicado: (2024)

Precision Glass Thermoforming Assisted by Neural Networks
por: Zhang, Yuzhou, et al.
Publicado: (2024)

NITO: Neural Implicit Fields for Resolution-free Topology Optimization
por: Nobari, Amin Heyrani, et al.
Publicado: (2024)

Scale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
por: Chiu, Pao-Hsiung, et al.
Publicado: (2026)

Extremization to Fine Tune Physics Informed Neural Networks for Solving Boundary Value Problems
por: Thiruthummal, Abhiram Anand, et al.
Publicado: (2024)

Physics-informed Partitioned Coupled Neural Operator for Complex Networks
por: Wu, Weidong, et al.
Publicado: (2024)

NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
por: Yagoubi, Mouadh, et al.
Publicado: (2024)

Towards Foundation Model for Chemical Reactor Modeling: Meta-Learning with Physics-Informed Adaptation
por: Wang, Zihao, et al.
Publicado: (2024)