Neural operators for accelerating scientific simulations and design
Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …
physical experiments. Numerical simulations are an alternative approach but are usually …
Recent progress of artificial intelligence for liquid-vapor phase change heat transfer
Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent
innovations in AI and machine learning uniquely offer the potential for collecting new types …
innovations in AI and machine learning uniquely offer the potential for collecting new types …
[HTML][HTML] VISION-iT: A framework for digitizing bubbles and droplets
Quantifying the nucleation processes involved in liquid-vapor phase-change phenomena,
while dauntingly challenging, is central in designing energy conversion and thermal …
while dauntingly challenging, is central in designing energy conversion and thermal …
CoDBench: a critical evaluation of data-driven models for continuous dynamical systems
P Burark, K Tiwari, MM Rashid, AP Prathosh… - Digital …, 2024 - pubs.rsc.org
Continuous dynamical systems, characterized by differential equations, are ubiquitously
used to model several important problems: plasma dynamics, flow through porous media …
used to model several important problems: plasma dynamics, flow through porous media …
Flowbench: A large scale benchmark for flow simulation over complex geometries
Simulating fluid flow around arbitrary shapes is key to solving various engineering problems.
However, simulating flow physics across complex geometries remains numerically …
However, simulating flow physics across complex geometries remains numerically …
Active Learning for Neural PDE Solvers
D Musekamp, M Kalimuthu, D Holzmüller… - arXiv preprint arXiv …, 2024 - arxiv.org
Solving partial differential equations (PDEs) is a fundamental problem in engineering and
science. While neural PDE solvers can be more efficient than established numerical solvers …
science. While neural PDE solvers can be more efficient than established numerical solvers …
Machine learning data practices through a data curation lens: An evaluation framework
Studies of dataset development in machine learning call for greater attention to the data
practices that make model development possible and shape its outcomes. Many argue that …
practices that make model development possible and shape its outcomes. Many argue that …
Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers
Mosaic Flow is a novel domain decomposition method designed to scale physics-informed
neural PDE solvers to large domains. Its unique approach leverages pre-trained networks …
neural PDE solvers to large domains. Its unique approach leverages pre-trained networks …
Artificial intelligence for liquid-vapor phase-change heat transfer
Artificial intelligence (AI) is shifting the paradigm of two-phase heat transfer research. Recent
innovations in AI and machine learning uniquely offer the potential for collecting new types …
innovations in AI and machine learning uniquely offer the potential for collecting new types …
DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks
We present DrivAerNet++, the largest and most comprehensive multimodal dataset for
aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with …
aerodynamic car design. DrivAerNet++ comprises 8,000 diverse car designs modeled with …