Library · Simulation & modelling
What Stochastic Modeling got wrong, from 51 theses
Summary written by a model from the records below: The records evaluate stochastic modeling across diverse applications including optimization, dynamic systems, forecasting, and machine learning. While stochastic methods capture uncertainty and noise, researchers frequently faced computational bottlenecks, high sampling variance, parameter unidentifiability, and cases where simpler deterministic baselines proved superior. These records come from graduate theses, 2021 to 2026, each labeled with its degree where the repository states it, and each links to its thesis. They were extracted by language models reading the full text, so treat each as a lead to read, not a verdict.
Plus 10 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Stochastic formulations fail to outperform simpler deterministic baselines
Stochastic optimization models and volatility formulations often yielded negligible improvements or lower accuracy compared to deterministic approaches. In several settings, deterministic baselines or thresholds were deliberately chosen to avoid the complexity and inferior empirical performance of stochastic alternatives.
Tried and failed
stochastic occupant behavior modeling applied to building energy performance simulation. Outcome: worse than baseline. Reason: failed to consistently outperform simplified deterministic models across simulation studies
Semantic Data Modeling for Quasi-Sentient Systems: A Framework for Holistic Knowledge-Based Automation · Georgia Tech · PhD
Tried and failed
two-stage stochastic programming applied to multi-sector energy system planning under climate uncertainty. Outcome: worse than baseline. Reason: value of the stochastic solution was negligible compared to expected value deterministic planning
Multi-system optimization : intermittent production, flexible demand, emerging technologies · UT Austin · PhD
Tried and failed
stochastic dynamic policy optimization applied to spatial dynamic closure decisions. Outcome: worse than baseline. Reason: training under stochastic conditions yielded no performance gain over deterministic baseline policies
Spatial and Behavioral Models for Ecological Management · Cornell · PhD
Tried and failed
chance-constrained stochastic optimization applied to resource provisioning and placement. Outcome: worse than baseline. Reason: under low variance conditions, stochastic methods provide no gain over deterministic empirical averages
On the Optimization of Edge Server Scaling and Placement for Open Radio Access Network (O-RAN) Slicing · Virginia Tech · Doctorate
Lost to a baseline
Deterministic interdependence modeling (CS5: €1,776/day) outperformed stochastic ARIMA uncertainty modeling without interdependence (CS2: €1,737.94/day).
Integrating Low-Carbon Technologies in Electricity Markets: Network-Constrained Bidding Strategies for Distribution System Aggregators · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected stochastic checkpoint strategies after proving theoretically that only a deterministic advancement boundary (Dirac delta function in time) maximizes arithmetic mean offspring.
Principles of checkpoint override · EPFL · PhD
Considered and rejected
Considered and rejected: Rejected modeling stochastic wave spectrum / probability distribution functions in favor of deterministic linear plane progressive wave theory.
Considered and rejected
Considered and rejected: Rejected stochastic/method-of-moments precipitation modelling in favor of a deterministic law-of-mass-action kinetic framework to keep coupled multi-physics analysis tractable.
An Integrative Approach to Dynamic Processes in Reverse Osmosis Desalination · Nottingham · Doctorate
Considered and rejected
Considered and rejected: Rejected stochastic sampling/dropout of Bayesian feature selection masks during test-time inference in favor of deterministic thresholding to obtain consistent subject diagnosis labels
Interpretable Machine Learning and Deep Learning Frameworks for Predictive Analytics and Biomarker Discovery from Multimodal Imaging Genetics Data · Johns Hopkins · PhD
Lost to a baseline
Stochastic Volatility (SV) models were beaten by deterministic GARCH-type models (FIGARCH/CGARCH/EGARCH) in out-of-sample forecasting for >60% of regions.
Financial risk forecasting : A case study of the Finnish housing market Rahoitusriskien ennustaminen : Sovelluksena Suomen asuntomarkkinat · University of Vaasa · PhD
Lost to a baseline
On the gap splits of UCI regression benchmarks, increasing the percentage of sampled stochastic parameters under HMC degraded predictive performance relative to MAP and lower-stochasticity models.
Uncertainty estimation with small and large models · Oxford · PhD
Lost to a baseline
Fully stochastic Wide-ResNet-28-10 trained with MFVI achieved lower accuracy (94.69% vs 95.61% deterministic on CIFAR-10; 77.68% vs 79.33% deterministic on CIFAR-100).
Uncertainty estimation with small and large models · Oxford · PhD
Tried and failed
Sample Average Approximation with Benders decomposition applied to stochastic vehicle routing assignment. Outcome: worse than baseline. Reason: failed to outperform deterministic benchmark within practical runtime budget
Dynamic Ridesharing under Travel Time Uncertainty: Passenger Preference and Optimal Assignment Methods · MIT · PhD
Lost to a baseline
During the wet first LEMA period (2013-2017), the stochastic RHP model achieved lower contribution margins than the deterministic model configured with long-term average precipitation.
Integrating Groundwater Conservation and Risk Mitigation under Uncertainty: Strategies for Sustainable Aquifers and Agriculture · Virginia Tech · Doctorate
Computational intractability and scenario explosion limit stochastic methods
Conditioning on full state histories, solving high dimensional control equations, and simulating large stochastic ensembles scale exponentially or become computationally intractable. As a consequence, exact expectations, transition probability matrices, and particle based stochastic methods were frequently abandoned.
Tried and failed
exact expectation conditioned on full state history applied to stochastic learning algorithms with Markov states. Outcome: infeasible cost. Reason: exponential growth of terms when conditioning on full historical state trajectories
The algorithmic learning equations · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected exact marginal likelihood and expectation-maximization (EM) optimization for PMF due to excessive computational intractability on large sparse space-time matrices, choosing Stochastic Gradient Descent (SGD) instead.
Data-driven and physics-informed wind prediction in offshore wind farms · Nottingham · PhD
Tried and failed
Direct time-domain simulation of fluctuating stochastic dipole ensembles applied to 3D far-field scintillation emission modeling. Outcome: infeasible cost. Reason: Simulating large ensembles of stochastic 3D dipoles in the time domain is computationally intractable.
Light-Matter Interactions with Photonic Quasiparticles · MIT · PhD
Tried and failed
treating recovery parameters as stochastic variables applied to uncertainty set computation. Outcome: infeasible cost. Reason: introduced combinatorial complexity and nonlinearities into computation without meaningful accuracy gains
Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems · EPFL · PhD
Tried and failed
finite difference method for partial differential equations applied to high dimensional stochastic optimal control. Outcome: infeasible cost. Reason: computational complexity scales exponentially with state dimension beyond low dimensional systems
Advancing frontiers of path integral theory for stochastic optimal control · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected full 3D steady-state stochastic analytical formulation (Appendix E) due to excessive required mathematical approximations, relying instead on numerical simulation.
Stability and reversibility of compressible fluid displacement in porous media · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected transported PDF methods solved via stochastic Monte-Carlo particles for practical engine simulations due to prohibitive computational expense.
Conditional source-term estimation evaluations for partially-premixed flames · Oxford · PhD
Considered and rejected
Considered and rejected: Rejected pure stochastic programming due to scenario explosion and scarce future renewable adoption data in favor of possibilistic-robust programming.
Optimal Operation and Planning of Multi-Energy Microgrids with Hydrogen Carriers Integration · University of Vaasa · PhD
Considered and rejected
Considered and rejected: Rejected calculation of the explicit state transition probability matrix P for the EV routing MDP due to stochastic driver behavior, dynamic traffic conditions, and unmodeled charging station wait times.
The digitalization of energy systems: towards higher energy efficiency · EPFL · PhD
Model misspecification and parameter unidentifiability degrade stochastic representations
Stochastic models frequently failed to identify parameters or reproduce empirical distributions and non random trends. In other cases, introducing stochasticity caused undesirable system behaviors like traffic oscillations or failed to track local physical concentrations.
Tried and failed
purely stochastic binary-choice drift diffusion model applied to baseline behavioral choice performance distributions. Outcome: did not generalise. Reason: fails to capture empirical spread of baseline performance without incorporating attentional state transitions
Decision Making, Behavioral Development and Fine Motor Control In Larval Zebrafish · Harvard · PhD
Tried and failed
single-value opportunity cost calculation applied to multi-metric stochastic portfolio evaluation. Outcome: did not generalise. Reason: scalar opportunity costs fail when evaluating multi-metric stochastic returns across heterogeneous scenarios
System of Systems Stakeholder Planning in a Multi-Stakeholder, Multi-Objective, and Uncertain Environment · Georgia Tech · PhD
Lost to a baseline
An MA(50) moving-average stochastic volatility model produced an unnaturally narrow 10-year FIA return distribution (SD of 0.006) similar to a constant-volatility GBM, failing to match historical FIA return dispersion compared to rough fractional Brownian motion models.
Selected Computational Problems In Insurance · York University · PhD
Considered and rejected
Considered and rejected: Rejected using pure stochastic Markovian/econometric models alone because they fail to exploit non-linear deterministic trends in historical pricing
Techno-economic analysis and method development applied to an aerobic gas fermentation and supercritical water gasification process · Nottingham · Doctorate
Considered and rejected
Considered and rejected: Purely stochastic WaveFunctionCollapse unit-block generation was rejected as an optimizer due to its inability to navigate non-random objectives effectively
Generative constructal design for thermal flow systems · Imperial · PhD
Considered and rejected
Considered and rejected: Decided against using stochastic simulation models in favor of numerical differential equation solving because stochastic models cannot easily track local spatial concentrations required for 2nd-order bimolecular reactions.
Dynamische Ionenaustauschchromatographie reaktiver Aluminium-Komplexe Dynamic ion exchange chromatography of reactive aluminium complexes · University of Marburg · PhD
Considered and rejected
Considered and rejected: Rejected adding stochastic error components to machine-learned motion planning models because it exacerbates traffic stop-and-go oscillations
Longitudinal Control for Self-driving Cars with Traffic Flow Considerations: Theory, Design, and Experiments · Georgia Tech · PhD
Considered and rejected
Considered and rejected: Rejected using annual consumption data because state-space models with stochastic volatility are poorly identified at annual frequency.
Essays on Asset Pricing and International Finance · University of Washington · PhD
Considered and rejected
Considered and rejected: Rejected the topographic elevation (η-based) classification for calibrating stochastic vegetation models because it yielded non-unimodal, irregular pdfs that degraded statistical moment evaluation compared to inundation probability (PI-based) classification.
Integrated approaches for monitoring and modeling vegetation in riparian and coastal environments · Politecnico di Torino · PhD
Tried and failed
stochastic process modeling of decision confidence applied to human perceptual discrimination datasets. Reason: model parameters were unidentifiable, leading to extremely low model recovery probabilities
How do humans give confidence? Comparing popular process models of confidence generation · Georgia Tech · PhD
Sampling variance and numerical distortions destabilize stochastic simulations
High variance in importance sampling and gradient estimation through random variables degraded algorithm convergence. Additionally, numerical discretization schemes dampened critical fluctuations or introduced phase divergence in stochastic dynamical systems.
Tried and failed
decision-focused learning with gradient backpropagation through sampling applied to network security game optimization. Outcome: worse than baseline. Reason: inaccurate gradient estimates from backpropagating through randomly sampled stochastic variables
Optimizing Decision-Making under Uncertainty -- A Data-Driven Perspective · Georgia Tech · PhD
Tried and failed
Monte Carlo scenario generation with rolling volatility applied to stochastic asset allocation models. Outcome: unstable. Reason: scenario generation failed uniformity tests under true empirical volatility without artificial parameter dampening
Improving time series and cross-sectional momentum trading strategies using stochastic programming · Iowa State · PhD
Lost to a baseline
Uniform sampling in stochastic gradient descent outperformed importance sampling after 5 iterations (1364 samples) because importance sampling suffered from high variance.
Decision-making for autonomous agents in adversarial or information-scarce settings · UT Austin · PhD
Considered and rejected
Considered and rejected: Rejected training stochastic differential equations by integrating dynamics and optimizing over full probability distributions or multi-Gaussian fits due to failure under significant inherent noise.
Modeling Dynamics of Classical and Quantum Systems Using Machine Learning Techniques · Tübingen · PhD
Tried and failed
finite horizon simulation for infinite horizon bounds applied to discounted stochastic optimization problems. Reason: truncation horizon was insufficient for high discount factor, producing statistical upper bounds lower than deterministic lower bounds
Risk neutral and risk averse stochastic optimization · Georgia Tech · PhD
Tried and failed
implicit Euler-Maruyama numerical integration applied to stochastic branching and extinction dynamics. Reason: Implicit time-stepping artificially dampened stochastic fluctuations, severely underestimating variance and missing discrete extinction events.
Mathematical and computational models of nuclear reactor start-up physics and operations · Imperial · PhD
Lost to a baseline
For high stochastic dimensions ($N=56$, $L_E/l=0.1$), sparse SC method reference error failed to compete with standard brute-force Monte Carlo simulation.
Numerical treatment of imprecise random fields in non-linear solid mechanics · Leibniz Universität Hannover · PhD
Lost to a baseline
Robbins-Monro Stochastic Approximation sieve baseline converged substantially slower than the proposed UQ gradient and accelerated gradient methods on the benchmark quadratic function under Monte Carlo noise.
Uncertainty quantification for optimisation, with applications to discrete problems and their convex relaxations · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected simulating historical catastrophic high-severity fire regimes (e.g., 1868 fire) because of high stochastic variance obscuring comparisons and uncertainty in post-harvest fuel loads.
Simulating Forest Dynamics, Disturbance, and Management in the Elliott State Research Forest, Oregon: A Comparative Study of Land Sharing, Land Sparing, and Triad Management · University of Oregon · Master's
Deterministic approximations fail to capture underlying stochastic dynamics
Continuous ordinary differential equation approximations and deterministic evaluations failed to capture noise induced saddle point escapes and extinction events. Ignoring stochasticity in these systems led to constraint violations, systematic overestimation, and inferior performance.
Tried and failed
rule-based behavior inference applied to human response behavior under disruption. Reason: produced systematic overestimation by ignoring baseline stochasticity in human behavior
Toward a Resilient Public Transportation System: Effective Monitoring and Control under Service Disruptions · MIT · PhD
Tried and failed
deterministic evaluation of learned stochastic policies applied to reinforcement learning policy evaluation. Outcome: worse than baseline. Reason: deterministic execution failed completely compared to stochastic execution of the policy
Advancing Data Science Methods for Environmental Health Policy Design and Evaluation · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected pure Linear Programming (MILP) mathematical modeling alone because it fails to adequately capture the stochastic nature of production and demand.
Supply Chain Simulation for Production Strategy Evaluation · MIT · Master's
Tried and failed
Continuous ODE numerical integration applied to Stochastic chemical reaction network dynamics. Outcome: no signal. Reason: Deterministic continuous approximation failed to capture transient stochastic intermediate phase transitions.
Programs and proofs: A molecular toolchain · Iowa State · PhD
Lost to a baseline
Continuum PDE baseline failed to capture stochastic extinction of infected cells and resulting tumour recurrence observed in discrete agent-based simulations under oscillatory regimes.
Improving the effectiveness of oncolytic virotherapy: insights from mathematical modelling · Politecnico di Torino · PhD
Theoretical inconsistencies and invalid calculus assumptions cause mathematical divergence
Stochastic formulations failed when treating stochastic integrals under classical calculus rules or when functional derivative operators diverged. Other models encountered mathematical infeasibility or lost detailed balance and dynamic consistency across decision stages.
Tried and failed
static coherent risk measures applied to multistage stochastic optimization. Reason: they fail dynamic consistency and recursivity across multiple decision stages
Some Unconventional Stochastic Programs · Georgia Tech · PhD
Tried and failed
equating functional derivative operators across stochastic calculi applied to infinite-dimensional martingale expansions. Reason: operators diverge without strict conditional expectation or adaptedness regularity constraints
Stochastic taylor expansions for functionals of martingales · Imperial · PhD
Considered and rejected
Considered and rejected: Stateful variance-reduced stochastic gradient estimates (e.g. SVRG) within standard discrete random walks, rejected because history dependence destroys detailed balance / exactness of the stationary distribution.
Efficient Sampling Using Markov Chain Monte Carlo Methods · University of Washington · PhD
Tried and failed
stochastic selective dissipation modeling applied to stochastic Lie-Poisson dynamical systems. Reason: anisotropic noise and mismatched dissipation prevent forming an exact Gibbs invariant measure without strict nilpotency or symmetric friction
On the rough diffusive limits of deterministic geometric mechanics · Imperial · PhD
Tried and failed
Stochastic reduced variational principles on semidirect products applied to ideal fluid dynamics. Reason: Hamiltonian is not hyper-regular with respect to the advected density parameter
Stochastic geometric mechanics for geophysical fluid dynamics and wave-current interactions · Imperial · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Rigorously prove that informative scheduling policies stochastically outperform non-informative counterparts across general queueing settings. Blocker: None
Information Freshness Optimization in Real-time Network Applications · Virginia Tech · Doctorate
Left open
Simulate routine screening effectiveness across various pathogen epidemiologic characteristics using the discrete-time stochastic network model. Blocker: None
Infectious Disease Surveillance and Control for Pandemic Prevention, Preparedness, and Response · Harvard · PhD
Left open
Derive analytical expressions for heteroplasmy temporal dynamics and mutant fixation probability across two or more coupled demes using the stochastic model. Blocker: None
Stochastic modelling and inference for evolution in ageing and infectious diseases · Imperial · PhD
Left open
Reformulate the compartmental epidemic model to incorporate parameter stochasticity using a Markov process instead of deterministic difference equations. Blocker: None
Strategies for Effective Mitigation of Infectious Diseases, with Focus on COVID-19 · Virginia Tech · Doctorate
Left open
Formulate robust geometric programming and chance-constrained optimization models for epidemic control on networks with stochastic transitions. Blocker: None
Learning and Control of Network Phenomena · Penn · PhD
Left open
Fit realistic seasonal transmission and recovery rate functions to empirical epidemiological datasets for stochastic epidemic models. Blocker: None
Infectious Disease Dynamics in Heterogeneous Populations: Stochastic, Seasonal, and Structured Epidemic Models with Intervention Strategies · Texas Tech · PhD
Left open
Formulate and solve two-stage or multi-stage stochastic programming models for prescriptive epidemiological resource allocation. Blocker: None
Large-scale Optimization for Robust Multi-Class Prediction and Resource Allocation · MIT · PhD
Left open
Simulate stochastic survival of the densest models on two- and three-dimensional spatial deme structures. Blocker: None
Stochastic modelling and inference for evolution in ageing and infectious diseases · Imperial · PhD
Left open
Fit the stochastic spatial SEIR-SEI metapopulation model against multi-country arbovirus case series and simulate global transmission dynamics beyond single-run approximations. Blocker: None
Modelling climate-driven spatiotemporal transmission dynamics of aedes-borne arboviruses · Imperial · PhD
Left open
Extend stochastic extinction and outbreak probability calculations to epidemic models with mass action incidence or large natural demographic turnover. Blocker: None
Effects of demographic, environmental and seasonal variability on disease outbreaks in stochastic vector-host, multi-patch and dengue epidemic models · Texas Tech · PhD
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- M Sharma · Oxford · PhD · 2023
Uncertainty estimation with small and large models - Ferdinando Insalata · Imperial · PhD · 2022
Stochastic modelling and inference for evolution in ageing and infectious diseases - Emadeldin Abbas Mazied Abdrabou · Virginia Tech · Doctorate · 2026
On the Optimization of Edge Server Scaling and Placement for Open Radio Access Network (O-RAN) Slicing - Vahid Shahbazbegian · University of Vaasa · PhD · 2026
Optimal Operation and Planning of Multi-Energy Microgrids with Hydrogen Carriers Integration - Matei-Cristian Ignuta-Ciuncanu · Imperial · PhD · 2025
Generative constructal design for thermal flow systems - Ellen Considine · Harvard · PhD · 2025
Advancing Data Science Methods for Environmental Health Policy Design and Evaluation - Fatima Javeed · Georgia Tech · PhD · 2025
Semantic Data Modeling for Quasi-Sentient Systems: A Framework for Holistic Knowledge-Based Automation - Qiuyun Yu · Virginia Tech · Doctorate · 2025
Integrating Groundwater Conservation and Risk Mitigation under Uncertainty: Strategies for Sustainable Aquifers and Agriculture - Mahmudul Bari Hridoy · Texas Tech · PhD · 2025
Infectious Disease Dynamics in Heterogeneous Populations: Stochastic, Seasonal, and Structured Epidemic Models with Intervention Strategies - Anna Poulton · Cornell · PhD · 2024
Spatial and Behavioral Models for Ecological Management - Theo Diamantakis · Imperial · PhD · 2024
On the rough diffusive limits of deterministic geometric mechanics - Byron Lev Mentis Cohen · Harvard · PhD · 2024
Infectious Disease Surveillance and Control for Pandemic Prevention, Preparedness, and Response
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