How has fractional calculus been applied in financial economics?4 answersFractional calculus has been applied in financial economics to describe and model complex financial time series. It has been used to develop stock financial market stochastic volatility and stock pricing models, incorporating the Taylor formula and the Brownian motion with drift terms. The use of fractional calculus equations based on the idea of decomposition-reconstruction-integration has improved the prediction accuracy of these models for financial time series. Additionally, fractional calculus has been used in the valuation of European style options when the underlying price process is driven by a fractional Lévy process. The option price is determined by a real-valued function satisfying a fractional partial differential equation, which is solved numerically using a weighted and shifted Grunwald approximation. These applications demonstrate the usefulness of fractional calculus in analyzing and predicting financial market dynamics.
How can deep learning be used to solve differential equations python code?4 answersDeep learning can be used to solve differential equations in Python code. Researchers have developed deep neural network (DNN) based methods, such as the Physics Informed Neural Network (PINN), to solve systems of ordinary and partial differential equations (ODEs and PDEs). These methods have been implemented using Python packages like DeepXDE and Tensorflow. By using deep feedforward neural networks and various activation functions and minimization methods, these models can accurately solve initial and boundary value problems. The neural networks can provide better accuracy for smaller numbers of grid points compared to traditional numerical methods like Runge-Kutta order four. Additionally, the nnde package in Python provides a pure-Python implementation of neural networks for solving differential equations, including systems of coupled partial differential equations.
How can deep learning be used to solve differential equations?5 answersDeep learning can be used to solve differential equations by incorporating the knowledge of the equation into the training process. This is done by adding a dedicated term to the loss function during the optimization procedure. The approach of using deep learning and neural networks to solve differential equations is known as physics-informed neural networks (PINNs). PINNs have been tested on various academic ordinary differential equations to highlight the benefits and drawbacks compared to standard integration methods. The use of PINNs allows for the possibility of using a minimal amount of training data, making it efficient for predicting solutions when the nonlinearity of the problem is weak. However, in strongly nonlinear problems, a priori knowledge of training data over some partial or the whole time integration interval is necessary. Additionally, deep learning frameworks like SHoP have been proposed to solve high-order partial differential equations by deriving high-order derivative rules for neural networks and providing explicit solutions for the equations.
What are some of the potential applications of fractional derivative models to the analysis of the economy growth?5 answersFractional derivative models have potential applications in analyzing economic growth. These models have been used to study the economic growth of countries in the Group of Twenty (G20) and have shown better performance compared to integer order models. By incorporating variables such as land area, population, exports, and government expenditure, fractional models can accurately predict the short-term evolution of a country's gross domestic product (GDP). The use of fractional derivatives does not require increasing the number of parameters and maintains the ability to predict GDP evolution in the short-term. This suggests that fractional derivative models can provide valuable insights into economic growth dynamics and aid in making accurate predictions.
What are the physical descriptions of the non-local conditions in fractional differential equations?5 answersThe physical descriptions of the non-local conditions in fractional differential equations vary depending on the specific problem being studied. In some cases, the non-local conditions involve nonlocal initial conditions, where the solution at a given point depends on the values of the solution and its derivatives at other points in the domain. In other cases, the non-local conditions may involve nonlocal boundary conditions, where the solution at a boundary point depends on the values of the solution and its derivatives at other points in the domain. These non-local conditions introduce additional complexity to the problem and require the use of specialized techniques, such as fixed point theorems and spectral radius estimation, to establish the existence, uniqueness, and continuous dependence of solutions.
What are some open problems in the field of fractional differential equations?4 answersOpen problems in the field of fractional differential equations include: 1) a list of 33 problems related to planar differential equations, Abel differential equations, difference equations, global asymptotic stability, geometrical questions, problems involving polynomials, and recreational problems with a dynamical component; 2) the existence of solutions of fractional differential equations with nonlinear boundary conditions, investigated using the monotone iterative method combined with lower and upper solutions; 3) the existence and uniqueness of solutions to nonlocal problems for fractional differential equations in Banach spaces, established using fractional calculus and fixed point method; 4) differential inequalities involving fractional derivatives in the sense of Riemann-Liouville, with bounds found using desingularization techniques and generalizations of Bihari-type inequalities; and 5) solving inverse problems for fractional differential equations, which are generally ill-posed and unstable, but important for modeling real-life problems and reducing errors in physical phenomena modeling.