Simulations & experiments

ODE(3)

• Extreme rotational events in a forced-damped nonlinear pendulum
  T3fidelity 0.05
  arXiv:2304.00039
• Equivalence of Mass Action and Poisson Network SIR Epidemic Models
  T3fidelity 0.05vs. high-degree-poisson-network· epidemiology, network disease dynamics
  arXiv:2310.13866
• The Lotka-Volterra Predator-Prey Model with Disturbance
  T2fidelity 0.00
  arXiv:2510.09628

Discrete-event (SimPy)(1)

• Asymptotically Optimal Appointment Scheduling in the Presence of Patient Unpunctuality
  T2fidelity 0.05vs. laplace-vs-normal-unpunctuality· healthcare operations / appointment scheduling
  arXiv:2412.18215

Agent-based(2)

• Heterogeneous RBCs via Deep Multi-Agent Reinforcement Learning
  T2vs. heterogeneous-agents· macroeconomic agent-based modelling
  arXiv:2510.12272
• BESTOpt: A Modular, Physics-Informed Machine Learning based Building Modeling, Control and Optimization Framework
  T3· building energy systems and control
  arXiv:2601.16283

PDE(1)

• Synthetic 1D Heat Equation Benchmark
  T2fidelity 0.92
  arXiv:synth-heat-1d

N-body(1)

• Synthetic Kepler Test
  T2fidelity 0.98
  arXiv:synth-kepler-test

Skipped or failed(10)

Papers we couldn't reproduce locally — usually because the method requires data or compute we don't have, or the extractor mapped it to a method class we haven't wired yet.

• Bifurcations in Delayed Lotka-Volterra Intraguild Predation Model
  method=ode · arXiv:1502.00841
• Parametric resonance induced chaos in magnetic damped driven pendulum
  method=ode · arXiv:1511.04593
• Resonance oscillation of a damped driven simple pendulum
  method=ode · arXiv:1712.01032
• Distributionally Robust Selection of the Best
  method=monte-carlo · arXiv:1903.05828
• The SIR model of an epidemic
  method=ode · arXiv:2104.12029
• Geometry of transit orbits in the periodically-perturbed restricted three-body problem
  method=n-body · arXiv:2203.16019
• Motion of a parametrically driven damped coplanar double pendulum
  method=ode · arXiv:2208.03292
• Lotka-Volterra predator-prey model with periodically varying carrying capacity
  method=monte-carlo · arXiv:2211.09276
• Stochastic spatial Lotka-Volterra predator-prey models
  method=monte-carlo · arXiv:2405.05006
• Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics
  method=pde · arXiv:2509.13425

Cross-field hypotheses(3 kept above threshold)

Pairs of papers from distinct fields where the synthesizer scored both novelty ≥ 0.5 and testability ≥ 0.4. Each entry proposes a specific mechanism transfer and an experiment we could run.

• building energy systems and control → epidemiology
  Physics-informed machine learning (PIML) priors embedded in compartmental epidemic models

  If we embed domain knowledge about disease transmission mechanisms (e.g., contact rates, incubation periods, recovery kinetics) as physics priors into data-driven SIR variants, we can improve epidemic forecasting accuracy and physical consistency when extrapolating beyond training data. This mirrors how BESTOpt embeds thermodynamic and HVAC physics into neural network modules to improve generalization.

  novelty 0.55testability 0.62business 0.35sim: ode
• building energy systems and control → simulation optimization under input uncertainty
  Physics-informed machine learning priors embedded in distributionally robust decision-making

  If we embed physics-based constraints and domain knowledge (from BESTOpt's PIML approach) into the ambiguity set construction and selection procedure of the RSB framework, we can reduce the conservatism of worst-case robust selection while maintaining guarantees under input distribution uncertainty. This would allow the robust selection procedure to exploit structural knowledge about the problem domain to tighten the ambiguity set and improve decision quality.

  novelty 0.55testability 0.62business 0.48sim: discrete-event
• healthcare operations / appointment scheduling → simulation optimization under input uncertainty
  Heavy-traffic fluid approximation for asymptotic optimization under distributional uncertainty

  If we apply Paper A's heavy-traffic fluid control problem (FCP) framework to Paper B's distributionally robust selection problem, we can derive asymptotically optimal selection policies that are robust to input distribution ambiguity. Specifically, we could formulate the worst-case selection criterion as a fluid-scale deterministic control problem, then solve it via discretized quadratic programming to obtain robust staffing or scheduling decisions.

  novelty 0.62testability 0.68business 0.55sim: discrete-event