Machine Learning Approximation Algorithms for High-Dimensional Fully Nonlinear Partial Differential Equations and Second-order Backward Stochastic Differential Equations

Mathematics

• Learning Algorithm 100%
• Backward Stochastic Differential Equation 99%
• Fully Nonlinear 95%
• Machine Learning 92%
• Nonlinear Partial Differential Equations 80%
• Approximation Algorithms 74%
• High-dimensional 69%
• Partial differential equation 61%
• Second order differential equation 26%
• Default Risk 24%
• Uncertainty 23%
• Model 23%
• Python 20%
• Transaction Costs 19%
• Nonlinear Phenomena 19%
• Portfolio Optimization 19%
• Neural Nets 19%
• Stochastic Gradient 18%
• Optimization Model 17%
• Gradient Descent 17%
• Volatility 15%
• Pricing 15%
• Industry 14%
• Valuation 14%
• Adjustment 14%
• Approximation Methods 13%
• Brownian motion 12%
• Optimization 10%
• Discretization 10%
• Numerical Results 9%
• Formulation 8%
• Derivative 8%
• Standards 7%
• Approximation 7%

Engineering & Materials Science

• Approximation algorithms 91%
• Partial differential equations 83%
• Differential equations 75%
• Learning algorithms 70%
• Machine learning 62%
• Brownian movement 15%
• Uncertainty 15%
• Derivatives 9%
• Costs 9%
• Neural networks 7%
• Industry 5%