Pages that link to "Item:Q2129550"
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The following pages link to Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows (Q2129550):
Displaying 10 items.
- A finite element/neural network framework for modeling suspensions of non-spherical particles. Concepts and medical applications (Q2022479) (← links)
- Fast simulation of particulate suspensions enabled by graph neural network (Q2083127) (← links)
- Physics-informed neural networks for gravity field modeling of the Earth and Moon (Q2138489) (← links)
- Physics-informed neural networks for high-speed flows (Q2175317) (← links)
- An integrated mechanistic-neural network modelling for granular systems (Q2504406) (← links)
- Machine learning the kinematics of spherical particles in fluid flows (Q4559220) (← links)
- Physically motivated structuring and optimization of neural networks for multi-physics modelling of solid oxide fuel cells (Q5070708) (← links)
- Artificial Neural Network (ANN) Model for Prediction of Mixing Behavior of Granular Flows (Q5451495) (← links)
- High-order Lagrangian algorithms for Liouville models of particle-laden flows (Q6614986) (← links)
- Accurate models of the added mass force of a uniform random distribution of spherical particles or bubbles (Q6669449) (← links)
