Library · Causal inference & statistics
What Sensitivity & Uncertainty Analysis got wrong, from 43 theses
Summary written by a model from the records below: Across engineering and scientific workflows, sensitivity and uncertainty analyses frequently encounter severe computational bottlenecks, data limitations, and inaccuracies caused by simplifying assumptions. Linearized approximations, ungrounded input distributions, and omitted parameter uncertainties often lead to constraint violations, distorted error bounds, and unmanageable simulation expenses. 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 9 problems the authors left open, listed at the end.
Failure modes, by theses reporting them
Select a mode to jump to its records.
Mathematical intractability and lack of empirical data hinder the construction of input uncertainty sets
Researchers rejected or abandoned complex uncertainty models and stochastic representations due to combinatorial complexity, intractable optimization formulations, and missing analytical tools. They also avoided Monte Carlo and probabilistic methods when empirical probability distributions could not be grounded from available data, because arbitrary distributions yielded misleading simulation outcomes.
Tried and failed
spatially varying uncertainty modeling in robust optimization applied to multiscale structural design. Outcome: infeasible cost. Reason: offered no statistical benefit over uniform uncertainty models while increasing computation by 22x
Methods for improving the robustness of optimised multiscale structures · Imperial · 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
multiple domain matrix concept generation applied to engineering systems design under uncertainty. Outcome: data insufficient. Reason: abandoned due to excessive complexity and data requirements
A novel data-driven approach to enhance analyse flexibility in engineering systems design under uncertainty · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected Monte Carlo simulation for uncertainty analysis because empirical probability distributions were not definable for novel single-year trial and pilot-scale datasets
Combined Mitigation Strategies in Irish Pasture-Based Dairy: A Six-Year Life Cycle Assessment · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected building a detailed empirical sensitivity analysis on the oil market model, because input uncertainties in mock data would prevent robust conclusions.
“We don’t want to be the bad guys”: Exploring the future of international oil companies in a ‘well below 2 degrees’ world · Imperial · PhD
Considered and rejected
Considered and rejected: Rejected using ungrounded or arbitrary input probability distributions in uncertainty analysis, as resulting simulation outcome distributions are ungrounded and misleading.
Approaches to occupant-centric building performance computing · TU Wien · PhD
Considered and rejected
Considered and rejected: Rejected using fuzzy-based MCDM to handle uncertainty due to mathematical complexity, lack of standard solution technique, and difficulty incorporating quantitative factors.
Methodologies for Frequency Stability Assessment in Low Inertia Power Systems · Politecnico di Torino · PhD
Considered and rejected
Considered and rejected: Rejected frequentist linear mixed models (e.g., lme4) and variational inference due to intractable or unreliable uncertainty estimation and p-value derivation in high-dimensional hierarchies.
Studying the tissue-specificity of cancer driver genes through KRAS and genetic dependency screens. · Harvard · PhD
Considered and rejected
Considered and rejected: Rejected modeling f*pi directly against a uniform measure when unnormalized, because prior elicitation for f*pi is difficult and causes unreliable uncertainty quantification
Probabilistic machine learning for aggregated and multivariate data · Imperial · PhD
Considered and rejected
Considered and rejected: Ellipsoidal uncertainty sets were rejected for semi-variance portfolios because the resulting optimization problem is not computationally tractable.
Essays on portfolio optimization and estimation risk · University College Dublin · PhD
Considered and rejected
Considered and rejected: Rejected enforcing diagonal sub-blocks in the structured uncertainty matrix due to lack of existing analysis and computational tools.
Feedback interconnection based input-output analysis of spatio-temporal response in wall-bounded shear flows · Johns Hopkins · PhD
Considered and rejected
Considered and rejected: Sobol sampling sensitivity analysis rejected because discretization of visual parameters violated the required uniform distribution assumption, prompting a switch to the Morris method.
COGNITIVE AGENTS FOR WAYFINDING UNDER UNCERTAINTY IN UNFAMILIAR INDOOR ENVIRONMENTS · Cornell · PhD
Considered and rejected
Considered and rejected: Rejected standard Multi-Objective Robust Decision Making (MORDM) due to lack of adaptive feedback and poor tractability under deep parameter uncertainty.
Ship and Naval Technology Trades-Offs for Science And Technology Investment Purposes · Georgia Tech · PhD
Local and linearized sensitivity approximations fail in nonlinear systems with strong parameter interactions
Linearized, one at a time, and derivative-based sensitivity methods broke down or were rejected because they could not capture higher-order parameter interactions, complex trade-offs, and output variance in nonlinear models. Similarly, adjoint and partial sensitivity techniques suffered from gradient explosion, poor convergence to global minima, or rapid divergence when indirect terms were omitted.
Tried and failed
adjoint sensitivity analysis applied to chaotic differential equations. Outcome: unstable. Reason: exponential sensitivity to initial conditions causes gradient explosion and unmanageable computational cost
Error behavior and optimal discretization of chaotic differential equations · MIT · PhD
Considered and rejected
Considered and rejected: Rejected local sensitivity analysis because it fails to capture higher-order parameter interactions compared to Sobol
Urban building energy modeling : from sensitivity to big data · UT Austin · PhD
Tried and failed
partial sensitivity modeling omitting indirect terms applied to temperature-dependent parameter uncertainty propagation. Outcome: worse than baseline. Reason: omitting indirect sensitivity terms broke error cancellation, causing faster divergence than uncorrected baseline
Tried and failed
adjoint sensitivity analysis with gradient descent applied to PDE-constrained inverse parameter estimation. Outcome: worse than baseline. Reason: Adjoint method had lower success converging to global minima and took significantly longer than forward sensitivity.
Data-driven Modeling of Lithium Intercalation Materials · MIT · PhD
Tried and failed
model linearization for global sensitivity analysis applied to coupled multidisciplinary engineering simulation. Reason: linearization failed to capture output variance and distorted variance-based sensitivity index rankings
Enabling End-to-End Sensitivity Analysis of Integrated Models · MIT · PhD
Considered and rejected
Considered and rejected: Rejected finite differencing for global sensitivity analysis due to slow runtime and poor linear approximations over non-linear performance spaces.
System-level design of low-carbon structures · MIT · PhD
Considered and rejected
Considered and rejected: Rejected sensitivity analysis in favor of multi-objective scenario-based planning because sensitivity analysis isolates individual variables without accounting for complex trade-offs.
Cost-Benefit Analysis of Anaerobic Digestion in Southern Ontario: A Case Study · University of Windsor · Master's
Considered and rejected
Considered and rejected: Rejected local sensitivity analysis methods (one-at-a-time, derivative-based) due to inaccuracy under non-linearities and parameter uncertainty.
Reliability and up-scaling of offshore wind turbines · Texas Tech · PhD
Tried and failed
ensemble sensitivity analysis for observation targeting applied to convective numerical weather prediction. Outcome: no signal. Reason: linear sensitivity metrics failed to predict actual non-linear variance reduction from data assimilation
An Examination of the Fundamental and Applied Aspects of Observation Targeting Strategies for Severe Convection · Texas Tech · PhD
Improper propagation strategies produce distorted or excessively conservative uncertainty bounds
Analytical uncertainty propagation, weak boundary imposition, and independent inversion sampling produced negative sensitivity indices, misaligned wake patterns, and distorted bounds across frequencies. Furthermore, robust control formulations and attributable effect analyses generated overly conservative uncertainty estimates that degraded operational performance or led to solver mismatches.
Tried and failed
second-order Sobol global sensitivity analysis applied to multi-input simulation models. Reason: Interacting input variables counteract each other's variance contributions, producing negative sensitivity indices.
A Methodology for Identifying Experiments For Uncertainty Mitigation in Complex Multi-Disciplinary Design · Georgia Tech · PhD
Tried and failed
Monte Carlo sampled independent deterministic inversions applied to correlated geophysical dispersion data. Reason: sampling strategy distorted output uncertainty bounds across frequencies
On the development of uncertainty-consistent one-dimensional shear wave velocity profiles from inversion of surface wave dispersion data · UT Austin · PhD
Tried and failed
Advanced Mean Value method for uncertainty analysis applied to MILP network optimization under uncertainty. Outcome: unstable. Reason: Non-monotonic CDFs and mismatch with Monte Carlo due to solver noise and run-time cutoffs
A Methodology for the Inclusion of Uncertainty in Space Logistics Campaign Planning and Optimization · Georgia Tech · PhD
Tried and failed
analytical uncertainty propagation through nonlinear reconstruction equations applied to plasma distribution function reconstruction. Reason: produced unrealistically large, unusable error estimates
The structure and dynamics of the near-sun solar wind · Imperial · PhD
Lost to a baseline
Weak imposition of stochastic boundary conditions failed to propagate boundary uncertainty into the interior near the inlet, yielding misaligned wake-only uncertainty and incorrect vortex shedding strengths compared to strong imposition with forced/unforced modes.
Global variance-based sensitivity analysis incurs prohibitive computational costs and convergence difficulties
Standard Sobol and global Monte Carlo techniques required excessive computational overhead and failed to achieve parameter convergence within feasible project timeframes. As a result, practitioners substituted simpler screening methods like Morris or one at a time analyses to prevent numerical instability and erroneous parameter rankings under restricted run budgets.
Lost to a baseline
The Sobol global sensitivity analysis method was outperformed by both the Morris method and VARS in parameter ranking reliability and convergence at lower computational budgets (1k-20k runs).
Barrier Island Morphodynamic Insights from Applied Global Sensitivity Analysis and Decadal Exploratory Modeling · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Restricting sensitivity analysis exclusively to the standard Sobol method was rejected because its high computational burden causes extreme numerical instability and erroneous parameter rankings under limited simulation budgets (<10,000 runs).
Barrier Island Morphodynamic Insights from Applied Global Sensitivity Analysis and Decadal Exploratory Modeling · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected computing arbitrary-order sensitivity analysis derivatives for Doppler broadened uncertainty quantification due to impractical computational and memory scaling.
Considered and rejected
Considered and rejected: Decided against Global Sensitivity Analysis (GSA) during initial tool development in favor of OAT sensitivity analysis due to computational simplicity.
Integrated Modeling and Optimization of Stormwater Control Measures for Sustainable Watershed Management Under Urbanization and Changing Rainfall Patterns · Virginia Tech · Doctorate
Considered and rejected
Considered and rejected: Rejected using global Monte Carlo sensitivity analysis due to the computational overhead and file setup complexity in WUFI Pro, choosing the Morris OAT method instead.
Hygrothermal Analysis of Vacuum Insulation Panels in New and Retrofit Walls for Canada · Carleton University · PhD
Pervasive measurement noise and input uncertainty overwhelm model discrimination and refinement
Efforts to calibrate parameters or discriminate between candidate models failed when measurement errors and baseline input uncertainties exceeded the magnitude of observed responses. Increasing model fidelity or adding modal frequency measurements also provided negligible uncertainty reduction because structural inadequacies and dominant input uncertainties masked higher-order physics.
Tried and failed
Reaction rate uncertainty quantification in fluid models applied to plasma discharge simulation. Reason: Kinetic uncertainties alone cannot bridge experimental deviations, pointing to transport or structural model inadequacies.
Modeling of intermediate-pressure argon capacitively coupled plasmas with uncertainty quantification · UT Austin · PhD
Tried and failed
parameter estimation using internal transient temperature profiles applied to packed bed thermal desorption models. Reason: energy balance model inadequacy caused large parametric uncertainty and inflated sensitivity metrics
Computational design of multi-sorbent adsorption processes for post-combustion carbon capture · Imperial · PhD
Tried and failed
error-bounded measurement falsification for model discrimination applied to structural dynamic response models. Outcome: no signal. Reason: measurement uncertainty exceeded the magnitude of the measured metric, preventing candidate model discrimination
Tried and failed
increasing model fidelity to reduce uncertainty applied to multidisciplinary engineering design. Reason: large input parameter uncertainties dominate, preventing higher fidelity physics models from reducing output predictive uncertainty
A Methodology for Identifying Experiments For Uncertainty Mitigation in Complex Multi-Disciplinary Design · Georgia Tech · PhD
Tried and failed
adding modal frequency data to model falsification applied to structural model uncertainty quantification. Outcome: no signal. Reason: natural frequency measurements provided negligible reduction in parameter and response prediction uncertainty
Surrogate and neural network uncertainty quantification yields overconfidence and physical inconsistencies
Monte Carlo dropout and over-parameterized ensemble variance produced paradoxically dropping uncertainty under severe noise, non-zero variance at optima, and violations of governing physical constraints. Other surrogate baselines either severely underestimated predictive uncertainty or failed to consistently improve upon standard deterministic confidence metrics.
Tried and failed
Monte Carlo dropout for uncertainty estimation applied to classification on noisy sensor inputs. Reason: models exhibited overconfidence under high input noise, causing predictive uncertainty to paradoxically drop as accuracy degraded
Reliable Sensor Intelligence in Resource Constrained and Unreliable Environment · Georgia Tech · PhD
Tried and failed
Monte Carlo dropout on physics-constrained neural networks applied to temperature and density depth profile modeling. Reason: Dropout perturbations broke structural constraints, causing physically inconsistent predictions during uncertainty estimation
Physics-informed Machine Learning with Uncertainty Quantification · Virginia Tech · Doctorate
Lost to a baseline
SVM with RBF achieved higher R² (0.9857) and lower MAE (3.57) than MCDNN (R² = 0.9726, MAE = 5.07), but was rejected due to severely underestimated uncertainty.
Leveraging Machine Learning Surrogates and Stochastic Optimization to Enhance Disaster-Responsive Pharmaceutical Supply Chains · Texas State University · Master's
Considered and rejected
Considered and rejected: Rejected ensemble parameter variance (Var[θ]) as an epistemic uncertainty metric due to over-parameterization yielding variance even at optima.
Structured, Constrained and Creative Learning · Tübingen · PhD
Lost to a baseline
Using Bayesian total uncertainty (predictive entropy) did not consistently beat deterministic or Bayesian average Maximum Softmax Probability (MSP).
OPEN-WORLD VIDEO STREAM FINGERPRINTING · Naval Postgraduate School · Master's
Omitting parameter and state uncertainty causes optimization infeasibility and severe interval undercoverage
Relying on deterministic scheduling or nominal model-based design of experiments caused system trajectories to violate physical voltage, temperature, and process constraints. Similarly, plugging point estimates into downstream Bayesian calculations or omitting baseline covariances yielded non-reproducible rates and severe undercoverage of credible intervals.
Tried and failed
nominal model-based design of experiments applied to systems with parametric uncertainty. Reason: ignoring parameter uncertainty caused designed experiment trajectories to violate process constraints and become infeasible
Algorithms and Tools for Feasibility Analysis and Optimal Experiment Design in Pharmaceutical Manufacturing · Imperial · PhD
Tried and failed
plugging point estimates into downstream Bayesian equations applied to low-rank matrix completion uncertainty quantification. Reason: ignoring parameter uncertainty in point estimates caused severe undercoverage of nominal credible intervals
New Guassian Process Modeling for Low-Rank and Simulated Data · Georgia Tech · PhD
Tried and failed
deterministic optimal scheduling under uncertainty applied to coupled multi-energy network dispatch. Outcome: did not generalise. Reason: ignoring stochastic uncertainty led to frequent voltage and temperature constraint violations in realization scenarios
Co-optimisation of electrical and district heating networks · Imperial · PhD
Tried and failed
least squares estimation without uncertainty modeling applied to kinetic rate parameter estimation. Outcome: unstable. Reason: lack of uncertainty modeling led to non-reproducible and biologically implausible fast rate estimates
The Kinetics of RNA Flow Across Subcellular Compartments · Harvard · PhD
Left open by the authors
Problems the authors named and did not get to.
Left open
Extend the Sobol' sensitivity analysis chaining framework to time-dependent dynamic systems governed by differential equations with multi-scale trajectory outputs. Blocker: Lacks specific mathematical formulation or chaining methodology for dynamic differential equation systems.
Enabling End-to-End Sensitivity Analysis of Integrated Models · MIT · PhD
Left open
Test whether the unstable component in space-split sensitivity analysis can be neglected across high-dimensional statistically homogeneous physical flow simulations. Blocker: Lacks specific test flow cases and concrete evaluation metrics for universal neglectability
Left open
Develop a computationally efficient method to compute conservative sensitivity analysis bounds for continuous exposures and continuous outcomes without signomial programming. Blocker: Lacks a specific mathematical formulation or concrete algorithmic approach to bypass signomial programming.
SENSITIVITY ANALYSIS METHODS FOR OBSERVATIONAL STUDIES WITH A CONTINUOUS EXPOSURE · Penn · PhD
Left open
Model implementation uncertainties in strategic water supply plans beyond stochastically varied antecedent climate conditions. Blocker: No specific formulation or implementation framework provided for modeling real-world implementation uncertainty
Optimizing Water System Resilience in Midsized Cities in Arid and Semi-Arid Regions · Texas Tech · PhD
Left open
Quantify uncertainty in history-matched fault permeability models using ensemble and probabilistic methods without ground truth data. Blocker: None
Numerical Modeling of Geologic Carbon Dioxide Storage in Faulted Siliciclastic Settings · MIT · PhD
Left open
Develop dynamic non-linear sediment rating curve models and hierarchical Bayesian models to quantify uncertainty in the variance discount factor. Blocker: None
Advancing watershed suspended sediment management by characterizing the multi-scale dynamics of sediment transport processes · Cornell · PhD
Left open
Perform uncertainty and parameter propagation analysis on the MCDM and KDE drought vulnerability framework to compute uncertainty bounds. Blocker: None
Susceptibility of agricultural production systems to climate extreme hazards with implications to the High Plains region · Texas Tech · PhD
Left open
Evaluate observational uncertainty propagation in tracer-aided sensitivity analysis and multi-objective hydrologic calibration frameworks. Blocker: None
Utility of stable isotope tracer data in large scale hydrologic modeling · University of Manitoba · PhD
Left open
Extend surrogate uncertainty quantification to include input, process, and material uncertainties, scaling to multi-physics process-structure-property-performance modeling in additive manufacturing. Blocker: Lacks specific implementation details, mathematical formulation, and proprietary training data/multi-physics simulation models from the thesis
Smart Quality Assurance System for Additive Manufacturing using Data-driven based Parameter-Signature-Quality Framework · Virginia Tech · Doctorate
Who has worked on this
Authors of the public theses cited on this page, most-cited first.
- Mathias Christian Haindl Carvallo · EPFL · PhD · 2026
Quantifying uncertainty and reliability of equivalent frame models for the seismic assessment of unreinforced masonry buildings - Rosemary K. Davidson · MIT · PhD · 2025
Enabling End-to-End Sensitivity Analysis of Integrated Models - Steven William Harvey Hoagland · Virginia Tech · Doctorate · 2024
Barrier Island Morphodynamic Insights from Applied Global Sensitivity Analysis and Decadal Exploratory Modeling - Efe Yamac Yarbasi · Georgia Tech · PhD · 2023
A Methodology for Identifying Experiments For Uncertainty Mitigation in Complex Multi-Disciplinary Design - Luis Alejandro Vergara Intriago · University College Dublin · PhD · 2026
Combined Mitigation Strategies in Irish Pasture-Based Dairy: A Six-Year Life Cycle Assessment - Isaac Charles Arseneau · Texas Tech · PhD · 2026
An Examination of the Fundamental and Applied Aspects of Observation Targeting Strategies for Severe Convection - Imtiajur Rahman · Texas State University · Master's · 2026
Leveraging Machine Learning Surrogates and Stochastic Optimization to Enhance Disaster-Responsive Pharmaceutical Supply Chains - Sai Pavan Polisetty · Imperial · PhD · 2025
Co-optimisation of electrical and district heating networks - Qi Yang · Cornell · PhD · 2025
COGNITIVE AGENTS FOR WAYFINDING UNDER UNCERTAINTY IN UNFAMILIAR INDOOR ENVIRONMENTS - Mariuxi Llanos · University of Windsor · Master's · 2025
Cost-Benefit Analysis of Anaerobic Digestion in Southern Ontario: A Case Study - Hossein Ahmadi · Virginia Tech · Doctorate · 2025
Integrated Modeling and Optimization of Stormwater Control Measures for Sustainable Watershed Management Under Urbanization and Changing Rainfall Patterns - Marin Vlastelica · Tübingen · PhD · 2025
Structured, Constrained and Creative Learning
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