ISOMORPH: A Supply Chain Digital Twin for Simulation, Dataset Generation, and Forecasting Benchmarks

arXiv:2605.12768 · stat.ML, cs.LG · Submitted 2026-05-12 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-05-12

Updated: 2026-09-18

Code: https://github.com/tuhinsahai/ISOMORPH

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Benchmark — 40× in this paper · explained in 2 episodes
• Foundation Model — 37× in this paper · explained in 3 episodes
• Digital Twin — 28× in this paper · explained in 5 episodes
• Foundation Models — 27× in this paper · explained in 7 episodes
• Conservation Laws — 19× in this paper · explained in 3 episodes
• Benchmarking — 7× in this paper · explained in 3 episodes
• Fidelity — 4× in this paper · explained in 2 episodes
• Time Series Forecasting — 4× in this paper · explained in 3 episodes
• Dynamic Bayesian Network — 3× in this paper
• Verification — 3× in this paper · explained in 2 episodes
• Core — 2× in this paper · explained in 2 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Aleatoric Uncertainty — 1× in this paper · explained in 5 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Decode — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• Mean Absolute Scaled Error — 1× in this paper
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
• Deepbullwhip: An Open-Source Simulation and Benchmarking for Multi-Echelon Bullwhip Analyses
• A decoder-only foundation model for time-series forecasting
• OR-Gym: A Reinforcement Learning Library for Operations Research Problems
• BULL-ODE: Bullwhip Learning with Neural ODEs and Universal Differential Equations under Stochastic Demand

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