The following pages link to Keras (Q27379):
Displaying 50 items.
- A physics-informed multi-fidelity approach for the estimation of differential equations parameters in low-data or large-noise regimes (Q2075654) (← links)
- A physically-informed deep-learning model using time-reversal for locating a source from sparse and highly noisy sensors data (Q2083682) (← links)
- Upscaling of two-phase discrete fracture simulations using a convolutional neural network (Q2085098) (← links)
- Error-correcting neural networks for semi-Lagrangian advection in the level-set method (Q2088344) (← links)
- Potential sales estimates of a new store (Q2089610) (← links)
- Perturbed iterate SGD for Lipschitz continuous loss functions (Q2093279) (← links)
- Learning-based model predictive current control for synchronous machines: an LSTM approach (Q2095284) (← links)
- Physics-based self-learning recurrent neural network enhanced time integration scheme for computing viscoplastic structural finite element response (Q2096901) (← links)
- Data-driven approach for dynamic homogenization using meta learning (Q2096905) (← links)
- Context-aware spatio-temporal event prediction via convolutional Hawkes processes (Q2102352) (← links)
- Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling (Q2112261) (← links)
- Neural eikonal solver: improving accuracy of physics-informed neural networks for solving eikonal equation in case of caustics (Q2112483) (← links)
- Data-driven predictions of the Lorenz system (Q2115546) (← links)
- Bridging the gap: machine learning to resolve improperly modeled dynamics (Q2116291) (← links)
- OptiLog: a framework for SAT-based systems (Q2118280) (← links)
- Constitutive artificial neural networks: a fast and general approach to predictive data-driven constitutive modeling by deep learning (Q2120033) (← links)
- On the antiderivatives of \(x^p/(1 - x)\) with an application to optimize loss functions for classification with neural networks (Q2122774) (← links)
- Robustness of LSTM neural networks for multi-step forecasting of chaotic time series (Q2122985) (← links)
- Meta-learning pseudo-differential operators with deep neural networks (Q2123371) (← links)
- Learning to differentiate (Q2123932) (← links)
- Deep learning of the spanwise-averaged Navier-Stokes equations (Q2123996) (← links)
- A long short-term memory embedding for hybrid uplifted reduced order models (Q2125587) (← links)
- Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (Q2127239) (← links)
- Inclusion of domain-knowledge into GNNs using mode-directed inverse entailment (Q2127250) (← links)
- Learning and correcting non-Gaussian model errors (Q2128493) (← links)
- Solving inverse-PDE problems with physics-aware neural networks (Q2129334) (← links)
- Physics-inspired architecture for neural network modeling of forces and torques in particle-laden flows (Q2129550) (← links)
- Latent-space inversion (LSI): a deep learning framework for inverse mapping of subsurface flow data (Q2130947) (← links)
- Beyond the Courant-Friedrichs-Lewy condition: numerical methods for the wave problem using deep learning (Q2131007) (← links)
- Coupling kinetic and continuum using data-driven maximum entropy distribution (Q2132630) (← links)
- Machine learning for fluid flow reconstruction from limited measurements (Q2134510) (← links)
- Automated porosity estimation using CT-scans of extracted core data (Q2147571) (← links)
- Mapping natural fracture networks using geomechanical inferences from machine learning approaches (Q2147575) (← links)
- Obey validity limits of data-driven models through topological data analysis and one-class classification (Q2147924) (← links)
- Self-triggered control of probabilistic Boolean control networks: a reinforcement learning approach (Q2159969) (← links)
- Probabilistic deep learning for real-time large deformation simulations (Q2160483) (← links)
- The deep parametric PDE method and applications to option pricing (Q2161843) (← links)
- Numerical approximation of singular forward-backward SDEs (Q2168288) (← links)
- Training thinner and deeper neural networks: jumpstart regularization (Q2170213) (← links)
- Interpreting deep learning models with marginal attribution by conditioning on quantiles (Q2172619) (← links)
- Block layer decomposition schemes for training deep neural networks (Q2173515) (← links)
- Interpreting stochastic agent-based models of cell death (Q2175264) (← links)
- CBSF: a new empirical scoring function for docking parameterized by weights of neural network (Q2183363) (← links)
- Improving RNA secondary structure prediction via state inference with deep recurrent neural networks (Q2183366) (← links)
- Time-series machine-learning error models for approximate solutions to parameterized dynamical systems (Q2184303) (← links)
- Data science applications to string theory (Q2187812) (← links)
- Deep global model reduction learning in porous media flow simulation (Q2187913) (← links)
- Distance geometry and data science (Q2192022) (← links)
- A deep learning approach to the inversion of borehole resistivity measurements (Q2192773) (← links)
- Learning on the edge: investigating boundary filters in CNNs (Q2193548) (← links)
