Pages that link to "Item:Q2101998"
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The following pages link to Learning by neural networks under physical constraints for simulation in fluid mechanics (Q2101998):
Displaying 16 items.
- Learning phase field mean curvature flows with neural networks (Q2083658) (← links)
- Three-dimensional realizations of flood flow in large-scale rivers using the neural fuzzy-based machine-learning algorithms (Q2084088) (← links)
- A physics-informed learning approach to Bernoulli-type free boundary problems (Q2107176) (← links)
- Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection (Q2133780) (← links)
- Physics-informed neural networks for high-speed flows (Q2175317) (← links)
- Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (Q2176917) (← links)
- A review on deep reinforcement learning for fluid mechanics (Q2245392) (← links)
- Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities (Q2683126) (← links)
- Physics informed neural networks: a case study for gas transport problems (Q2699348) (← links)
- Learned turbulence modelling with differentiable fluid solvers: physics-based loss functions and optimisation horizons (Q5038552) (← links)
- Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks (Q5042008) (← links)
- Machine learning active-nematic hydrodynamics (Q5073282) (← links)
- Partial differential equations for oceanic artificial intelligence (Q6053385) (← links)
- Transformers for modeling physical systems (Q6055222) (← links)
- Learning Specialized Activation Functions for Physics-Informed Neural Networks (Q6143615) (← links)
- A thermodynamics-informed active learning approach to perception and reasoning about fluids (Q6164293) (← links)
