FireModel-AI
Reduced-order models for complex physics and finance.
FireModel-AI is a research platform focused on dimensionality reduction, surrogate modeling, and AI-assisted prediction of computationally expensive systems.
The platform is designed to accelerate high-dimensional simulations and data-driven forecasting workflows using reduced-order modeling, neural networks, and scientific machine learning.
Developed by Mehrdad Nouroozyan.
Platform Focus
The project combines numerical modeling, data-driven reduction, and machine learning to build fast predictive models for high-dimensional systems.
Reduced-Order Modeling
Compress complex simulation data into low-dimensional representations while retaining the dominant system behavior.
Surrogate Intelligence
Replace expensive full-order evaluations with fast learned models suitable for rapid exploration and prediction.
Complex Physics
Support modeling workflows involving nonlinear, transient, and high-dimensional physical systems.
Quantitative Finance
Extend reduced-order and AI-based prediction concepts toward financial modeling, risk analysis, and data-driven forecasting.
Scientific Foundation
POD + ANN reduced-order modeling concept
High-dimensional snapshots are arranged into a data matrix and compressed into a reduced basis using Proper Orthogonal Decomposition or related dimensionality-reduction techniques.
A machine-learning surrogate then approximates the mapping from input parameters to reduced coefficients.
The predicted high-dimensional response is reconstructed from the reduced representation:
This workflow enables rapid prediction of complex system responses without repeatedly solving the full high-dimensional model.