Pages that link to "Item:Q2671220"
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The following pages link to Deep neural networks algorithms for stochastic control problems on finite horizon: numerical applications (Q2671220):
Displaying 29 items.
- Solving high-dimensional Hamilton-Jacobi-Bellman PDEs using neural networks: perspectives from the theory of controlled diffusions and measures on path space (Q825596) (← links)
- Recurrent neural networks for stochastic control problems with delay (Q2061009) (← links)
- An application of the splitting-up method for the computation of a neural network representation for the solution for the filtering equations (Q2093308) (← links)
- Deep combinatorial optimisation for optimal stopping time problems: application to swing options pricing. (Q2094859) (← links)
- Using stochastic programming to train neural network approximation of nonlinear MPC laws (Q2097845) (← links)
- Automatic model training under restrictive time constraints (Q2108929) (← links)
- Deep learning for constrained utility maximisation (Q2152236) (← links)
- Deep learning as optimal control problems: models and numerical methods (Q2297872) (← links)
- A mean-field optimal control formulation of deep learning (Q2319864) (← links)
- Linear-quadratic stochastic delayed control and deep learning resolution (Q2664898) (← links)
- Numerical resolution of McKean-Vlasov FBSDEs using neural networks (Q2684929) (← links)
- Performance of a Markovian neural network versus dynamic programming on a fishing control problem (Q2699283) (← links)
- Semiglobal optimal feedback stabilization of autonomous systems via deep neural network approximation (Q4999517) (← links)
- An Algorithm to Construct Subsolutions of Convex Optimal Control Problems (Q5039275) (← links)
- Discrete-Time Portfolio Optimization under Maximum Drawdown Constraint with Partial Information and Deep Learning Resolution (Q5050082) (← links)
- Error Estimates for a Tree Structure Algorithm Solving Finite Horizon Control Problems (Q5056666) (← links)
- SympOCnet: Solving Optimal Control Problems with Applications to High-Dimensional Multiagent Path Planning Problems (Q5058288) (← links)
- Deep Neural Networks Algorithms for Stochastic Control Problems on Finite Horizon: Convergence Analysis (Q5151934) (← links)
- Value-Gradient Based Formulation of Optimal Control Problem and Machine Learning Algorithm (Q6040292) (← links)
- Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning (Q6047503) (← links)
- Deep empirical risk minimization in finance: Looking into the future (Q6054448) (← links)
- Neural networks for first order HJB equations and application to front propagation with obstacle terms (Q6087416) (← links)
- Deep reinforcement learning in finite-horizon to explore the most probable transition pathway (Q6118140) (← links)
- Optimal liquidation through a limit order book: a neural network and simulation approach (Q6164829) (← links)
- PDNNs: the parallel deep neural networks for the Navier-Stokes equations coupled with heat equation (Q6537439) (← links)
- Lax-Oleinik-type formulas and efficient algorithms for certain high-dimensional optimal control problems (Q6575313) (← links)
- Swing contract pricing: with and without neural networks (Q6581630) (← links)
- Recent developments in machine learning methods for stochastic control and games (Q6615618) (← links)
- A deep learning method for incompressible fluid equations and the coupling problems (Q6665361) (← links)
