9 papers
Multiscale passive scalar turbulence in a compressed subspace via tensor trains
Stefano Pisoni, Egor Tiunov, Chiara Calascibetta
Capturing the multiscale statistics of turbulence in compressed form remains a central challenge for reduced-order modeling. We introduce a hybrid Tensor Train (TT) approach for a…
Tensor-network approach to quantum optical state evolution beyond the Fock basis
Nikolay Kapridov, Egor Tiunov, Dmitry Chermoshentsev
Understanding the quantum evolution of light in nonlinear media is central to the development of next-generation quantum technologies. Yet, modeling these processes remains computa…
Efficient upsampling for tensor-network and quantum-state encoded functions
Siddhartha E. Guzman, Egor Tiunov, Leandro Aolita
Both tensor trains (TTs) and quantum states provide compressed representations of grid-structured data with potentially exponential compression power. We present a unified framewor…
Quantum-inspired space-time PDE solver and dynamic mode decomposition
Raghavendra Dheeraj Peddinti, Stefano Pisoni, Narsimha Rapaka +4
The curse of dimensionality is ubiquitous in both numerical and data-driven methods. This is particularly severe for space-time methods, which treat the combined space-time domain…
A quantum-inspired multi-level tensor-train monolithic space-time method for nonlinear PDEs
N. R. Rapaka, R. Peddinti, E. Tiunov +5
We propose a multilevel tensor-train (TT) framework for solving nonlinear partial differential equations (PDEs) in a global space-time formulation. While space-time TT solvers have…
Compression, simulation, and synthesis of turbulent flows with tensor trains
Stefano Pisoni, Raghavendra Dheeraj Peddinti, Egor Tiunov +2
Numerical simulations of turbulent fluids are paramount to real-life applications, from predicting and modeling flows to diagnostic purposes in engineering. However, they are also…