Machine Learning Methods for Stochastic Differential Equations
A Special Issue of Axioms (ISSN 2075-1680) belonging to the section "Mathematical Analysis".
Deadline for manuscript submissions: 31 March 2027 | Viewed by 439
Editor
Interests: multifractional process; statistical inferences; Malliavin calculus; stochastic differential equations; stochastic modeling; process simulation; unsupervised learning on processes; approximation theory; graph theory
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue is motivated by the application of machine learning to financial data. Much financial data is described by stochastic differential equations. Machine learning in stochastic processes as stochastic differential equations has received attention for several years. As machine learning has developed, new tools have been discovered to study these equations. This Special Issue welcomes papers showcasing novel findings in modeling stochastic process data using stochastic differential equations and innovative machine learning methods for solving these equations, either based on traditional stochastic calculus or on machine learning algorithms. The Special Issue also aims to shed some light on the applications of stochastic calculus in a wide range of fields, such as finance, physics, electrical engineering, and biostatistics.
Dr. Qidi Peng
Guest Editor
Manuscript Submission Information
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Keywords
- stochastic calculus
- fractional calculus
- Malliavin calculus
- stochastic differential equations
- numerical methods
- machine learning algorithm
- finance
- engineering
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