Integration of physics-based models with machine learning techniques to solve complex scientific problems and discover governing equations from data.
Advanced algorithms for multiway data analysis in high-dimensional spaces with focus on tensor decompositions and efficient computational methods.
Adaptive recurrent neural networks for solving time-varying tensor equations and development of specialized architectures for scientific computing.
High-performance algorithms for large-scale matrix and tensor computations with development of efficient solvers for scientific computing applications.
Adaptive wavelet algorithms for solving PDEs, integral equations, and signal processing problems with focus on fractional-order differential equations.
Quantum algorithms for tensor computations and numerical linear algebra with exploration of quantum advantage in high-dimensional scientific computing.
Interactive tool for tensor operations, decompositions, and visualizations with real-time computation.
Launch DemoAdvanced wavelet analysis tool for signal processing and solving fractional differential equations.
Launch DemoDemonstrate physics-informed machine learning models for solving scientific computing problems.
Launch Demo
