Pages that link to "Item:Q6110192"
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The following pages link to Learning black- and gray-box chemotactic PDEs/closures from agent based Monte Carlo simulation data (Q6110192):
Displaying 7 items.
- Neural network approach to data-driven estimation of chemotactic sensitivity in the Keller-Segel model (Q2092227) (← links)
- Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data (Q6086911) (← links)
- A novel physics-informed deep learning strategy with local time-updating discrete scheme for multi-dimensional forward and inverse consolidation problems (Q6121797) (← links)
- Remark on the entropy production of adaptive run-and-tumble chemotaxis (Q6196329) (← links)
- Slow invariant manifolds of singularly perturbed systems via physics-informed machine learning (Q6573172) (← links)
- RandONets: shallow networks with random projections for learning linear and nonlinear operators (Q6648362) (← links)
- Slow invariant manifolds of fast-slow systems of ODEs with physics-informed neural networks (Q6661630) (← links)
