- A numerical model of superspreading surfactants on hydrophobic ...🔍
- An Investigation of Computational Models for Surfactant Self|Assembly🔍
- Data|driven prediction of the performance of enhanced surfaces ...🔍
- Using Data for Increased Realism with Haptic Modeling and Devices🔍
- Physics|Guided Data|Driven Modeling for Control in Additive ...🔍
- Investigation of Surfactant Efficiency Using Dissipative Particle ...🔍
- Data|driven low|dimensional model of a sedimenting flexible fiber🔍
- Interpretable and Explainable Data|Driven Methods for Physical ...🔍
Using data|driven models to simulate the performance of surfactants ...
A numerical model of superspreading surfactants on hydrophobic ...
Among them, we propose a computational fluid dynamics model, based on the volume of fluid technique, with the piecewise linear interface ...
An Investigation of Computational Models for Surfactant Self-Assembly
The model can approximate the temperature dependence for two cationic surfactants without direct parameterization. The phase space of an atomistic model used to ...
Data-driven prediction of the performance of enhanced surfaces ...
... modeling of aerodynamic simulations for multiple operating conditions using machine learning AIAA J. 56 3622–35. Go to reference in article ...
Using Data for Increased Realism with Haptic Modeling and Devices
Heather Culbertson, USC May 20, 2022 The haptic (touch) sensations felt when interacting with the physical world create a rich and varied ...
Physics-Guided Data-Driven Modeling for Control in Additive ...
... with many AM processes, it is often difficult to physically model these processes for control. In this talk, I examine how data obtained from ...
Investigation of Surfactant Efficiency Using Dissipative Particle ...
With a simple surfactant model, we investigate how variations in size and structure of surfactants influence their ability to reduce the interfacial tension. In ...
DDPS | Data-driven methods for fluid simulations in computer graphics
Fluid phenomena are ubiquitous to our world experience: winds swooshing through trembling leaves, turbulent water streams running down a ...
Data-driven low-dimensional model of a sedimenting flexible fiber
In this paper, we describe a data-driven technique to create high-fidelity low-dimensional models of flexible fiber dynamics using machine learning.
Interpretable and Explainable Data-Driven Methods for Physical ...
Description: A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential in ...
Surfactants – Larson Lab - University of Michigan
We use a variety of simulations, including atomistic and coarse-grained molecular dynamics and mesoscopic simulations, to study the flow behavior and structure ...
Advancing Reacting Flow Simulations with Data-Driven Models ...
... models, to embody in them all the prior knowledge and physical constraints that can enhance their performances, and to improve them based on ...
Building Better Models Faster with Synthetic Data - YouTube
... models and how you can use new techniques to generate high quality synthetic data to fine-tune highly accurate SLMs for your use case.