Scientific machine learning and reduced-order modeling framework for fast approximation of complex dynamical systems, high-dimensional numerical simulations, and parameter-dependent physical processes.
FireModel-AI combines data-driven surrogate modeling, reduced-order modeling, and machine learning techniques to accelerate the approximation and reconstruction of computationally expensive numerical simulations.
Projection-based and latent-space reduction techniques for compact representation of high-dimensional systems.
Data-driven surrogate models for fast approximation of parameter-dependent dynamical responses.
Designed for integration with large-scale numerical simulations and computational workflows.
Rapid prediction and reconstruction capabilities for complex transient systems.
FireModel-AI is designed as a flexible scientific machine learning framework that can conceptually integrate different model families for reduction, approximation, signal analysis, and feature extraction.
Nonlinear dimensionality reduction using encoder-decoder mappings.
Data-driven approximation of nonlinear parameter-to-response mappings.
Frequency response representation between input and output signals.
Spectral decomposition of transient signals into frequency-domain components.
Feature extraction from spatial or structured simulation fields.
Projection of high-dimensional states onto a compact reduced basis.
The framework can be adapted to a broad range of scientific and engineering problems involving complex dynamical behavior and computationally intensive simulations.
A high-dimensional system response can be approximated within a reduced latent representation:
The dominant structures of the original system are represented in a low-dimensional basis. A surrogate model estimates the reduced coefficients, allowing rapid reconstruction of the system response while preserving the dominant physical behavior.
Illustration of a generic reduced-order modeling workflow.
Developed by Mehrdad Nouroozyan within ongoing research activities in scientific machine learning, reduced-order modeling, computational modeling, and data-driven engineering analysis.
The framework focuses on bridging high-fidelity numerical simulation workflows with efficient machine learning approximations for scalable scientific computing applications.