This repository collects research papers of large Foundation Models for Scenario Generation and Analysis in Autonomous Driving. The repository will be continuously updated to track the latest update.

Tools for Stochastic Simulation using diffusion models (R).

ORNL’s Real-Twin project is a streamlined scenario generation tool that automatically integrates real-world traffic data to create high-fidelity digital twins for simulating the impacts of connected and automated vehicles in microsimulation environments.

Populating agent-based models with agents who give rise to dynamics and scenarios of interest

Implemented stochastic CVaR model for the optimal asset allocation together with the Bootstrapping and the Monte Carlo scenario generation methods.

This project aims to synthesize realistic traffic scenarios from specifications in Temporal Logic. This work was done as part of my thesis @ TUM.

YamlPrompt is a lightweight generative pipeline that enriches YAML files by replacing selected fields with AI‑generated text or images.

Models for India Power Markets

AI-native BDD toolkit for Claude Code. Generate Gherkin scenarios, validate syntax, detect anti-patterns, scaffold step definitions.

An AI-powered interactive storytelling platform that generates dynamic scenarios and narratives using local and cloud LLMs. Features scenario generation, memory management, and hopefully multi-provider LLM support.

Scripts and data used for the master's thesis "Portfolio selection with ES and regular vine copulae with EVT marginals" presented at the Wirtschaftsuniversität Wien in summer 2023. Code is structured for the purposes of the thesis and may not comply with best coding standards.

A two stage portfolio optimizer, researching if using synthetic scenarios produce better results.

End-to-end Python framework for robust pension fund management under parameter uncertainty. Implements three Distributionally Robust (DRO) Asset-Liability Management (ALM) formulations (Mixture, Box, Wasserstein) with GBM scenario generation, convex optimization (LP/SOCP), and comprehensive backtesting. Based on 2026 research by Ghahtarani et al.

Interactive Databricks App demonstrating financial forecasting, what-if scenario testing, and revenue runway estimation using Lakehouse architecture.

This repo explores ways of using meta-programming to run pytest with different scenarios

The repository for the master thesis "Multimodal Retrieval-Augmented Generation for Autonomous Driving Scenario Generation"