Predicts fracture evolution and material failure in brittle materials.
problem Predicting how fractures propagate and materials fail in brittle materials.
method Recurrent graph convolutional neural networks trained on simulation data.
result Predictions within 3% for fracture damage and 15% for time to failure.
Paper presents ML approaches for faster brittle fracture modeling.
problem Faster modeling of brittle fracture in concrete.
method Machine learning algorithms combined with physics-based assumptions.
result ML models are orders of magnitude faster than high-fidelity models.
Optimizes structure topology for ductile and brittle fracture resistance.
problem Minimizing mass while ensuring structural damage and fracture resistance.
method Phase-field approach for modeling fracture, level-set topology optimization.
result Enhanced fracture resistance through two formulations.
Enhanced PINN for brittle fracture modeling using transfer learning.
problem Solving brittle fracture problems in physics.
method Physics-informed neural network (PINN) with variational energy minimization and transfer learning.
result The proposed approach yields better accuracy in predicting crack paths compared to conventional PINN.
Machine learning predicts failure in brittle materials with high accuracy.
problem Predicting failure in brittle materials under repetitive loads.
method Phase-field model combined with supervised machine learning.
result Framework predicts failure with acceptable accuracy even in noisy data.
DAFNO learns surrogates for complex systems on irregular geometries.
problem Learning accurate surrogates for complex physical systems on irregular geometries.
method DAFNO incorporates a smoothed characteristic function in the integral layer architecture of FNOs, leveraging FFT for rapid computations.
result DAFNO achieves state-of-the-art accuracy on material modeling and airfoil simulation datasets.
Study models fractures in porous media using geometric analysis.
problem Analyzing fluid flow in fractures with complex geometries.
method Developed a geometric model using Riemannian manifold and Laplace Beltrami operators.
result Reduced model accurately approximates flow in complex fractures.
Efficient deep learning model classifies fractures from X-rays.
problem Manual X-ray examination of fractures is time-consuming and error-prone.
method Robust training loop using transfer learning and latest dataset.
result Model achieves superior performance in less than ten epochs.
Machine learning reduces DFN size by 80% for faster simulations.
problem Simulating flow and transport in large DFNs is computationally intensive.
method Graph theory and machine learning to identify a smaller, representative network.
result Reduced network size by approximately 20% without losing breakthrough curves.
The paper uses ML to predict oil rate post-HF, comparing it to engineers' predictions.
problem Predicting oil rate post-hydraulic fracturing.
method Data-driven model using ML techniques on fracturing job data.
result ML predictions outperform engineers' predictions.
Generalized meshes for non-regular geometries, including fractures.
problem Discretization of partial differential equations in non-regular geometries.
method Introduces generalized meshes with overlapping elements and flexible adjacency relations.
result Discrete differential forms on virtually inflated meshes characterize the trace space of forms in surrounding volumes.
Deep learning system detects hip fractures as well as radiologists.
problem Automatically identifying hip fractures from x-rays.
method Trained on 53,000 clinical x-rays, system achieves 0.994 ROC curve.
result Equivalent diagnostic performance to human radiologists.
This paper analyzes uncertainty in DFN simulations using sensitivity analysis.
problem Uncertainty in estimating QoI due to epistemic and aleatoric uncertainties in DFN simulations.
method Sensitivity analysis to attribute uncertainty to input parameters and aleatoric uncertainty.
result Characterizes uncertainty in DFN flow simulations with heteroskedastic aleatoric uncertainty.
Subsurface applications including geothermal, geological carbon sequestration, oil and gas, etc., typically involve maximizing either the extraction of energy or the storage of fluids. Characterizing the subsurface is extremely complex due to heterogeneity and anisotropy. Due to this complexity, there are uncertainties…
Fractured Sampling improves LLM reasoning efficiency by truncating CoT trajectories.
problem Efficiently scaling reasoning in large language models with limited tokens.
method Integrating truncated Chain-of-Thought (CoT) with Fractured Sampling across multiple dimensions.
result Fractured Sampling achieves superior accuracy-cost trade-offs compared to full CoT.
We use topological methods to prove a semicontinuity property of the Hodge spectra for analytic germs defined on an isolated surface singularity. For this we introduce an analogue of the Seifert matrix (the fractured Seifert matrix), and of the Levine--Tristram signatures associated with it, defined for null-homologous…
Optimal design portfolios improve energy efficiency and reduce risk in uncertain reservoirs.
problem Uncertain reservoir conditions lead to unstable gas recovery and low resource efficiency.
method Developed optimal portfolios of well designs based on reservoir conditions and probabilities.
result Remarkable reduction in variation and substantial increase in energy efficiency achieved.
The paper highlights AI brittleness and the need for robust testing out-of-distribution performance.
problem The brittleness of AI systems, especially Deep Neural Networks, limits their reliability and certification.
method Analysis of AI brittleness and OOD performance, emphasizing the need for resilience and improved evaluation methods.
result AI systems are more failure-prone than certified in critical systems, and OOD performance falls off gradually.
Neural program analyzers are brittle and sensitive to input changes.
problem Reliability of neural program analyzers in software engineering.
method Developing testing techniques for neural program analyzers.
result Simple input perturbations can cause neural program analyzers to make mistakes.
We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that k nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with O(sqrt((k + ln n) / n)) error bound range for n in-sample examples.
New method fractures hyperbolic manifolds using cone singularities.
problem Deforming hyperbolic manifolds with cone singularities.
method Direct manipulation of a fundamental polyhedron to change cone angles.
result Upper unknotting tunnels of highly twisted links can be drilled out.
Study identifies key parameters and input dimensions making LLMs and VLMs brittle.
problem Vulnerability of large language and vision-language models to perturbations.
method Proposed FI measure based on information geometry to quantify sensitivity.
result Small subset of high FI parameters significantly contribute to brittleness.
Universal model for soft tissue mechanics under shock waves.
problem Modeling shock wave mechanics in soft biological tissues.
method Continuum mixture theory with phase-field mechanics.
result Universal thermodynamically consistent formulation for soft porous tissues.
New method adds noise to rewards to improve reinforcement learning performance.
problem Brittleness in reinforcement learning due to variance differences between states and actions.
method Adaptive Symmetric Reward Noising (ASRN) by adding Gaussian noise to rewards based on their variance.
result ASRN improves reinforcement learning performance in various tasks, including autonomous driving.
This work examines overfitting in RL, offering new perspectives and practical solutions.
problem Overfitting in RL, especially in continuous domains.
method Examines overfitting in MDPs, proposes methods to reduce risks.
result Offers practical observations for RL researchers and practitioners.
Improves efficiency of simulators that fail to return.
problem Computational inefficiency in simulators that don't return for certain inputs.
method Trains a conditional normalizing flow to propose perturbations.
result Increased computational efficiency of simulators.
ARO overfits by making constraints dependent on uncertainty, leading to brittleness.
problem ARO's adaptive policies become brittle when realizations fall outside the uncertainty set.
method Assigning constraint-specific uncertainty set sizes with probabilistic guarantees.
result Regularization through specific uncertainty set sizes ensures stability and flexibility.
Paper develops a classification method using matrix-variate t-distributions.
problem Classifying matrix-valued observations with dependence structure.
method Develops an Expectation-Maximization algorithm for discriminant analysis.
result Method shows promise on various datasets.
We propose a Markov jump process with the three-state herding interaction. We see our approach as an agent-based model for the financial markets. Under certain assumptions this agent-based model can be related to the stochastic description exhibiting sophisticated statistical features. Along with power-law probability …
New method robust to semi-random sparse recovery, nearly-linear time.
problem Brittleness of fast sparse recovery algorithms under generative model changes.
method Designing a new iterative method robust to semi-random model.
result Proves robustness of new method to semi-random generative models.
The goal of this paper is to assess the utility of Reduced-Order Models (ROMs) developed from 3D physics-based models for predicting transient thermal power output for an enhanced geothermal reservoir while explicitly accounting for uncertainties in the subsurface system and site-specific details. Numerical simulations…
Transformers simulate finite-state automata with fewer layers.
problem How do shallow, non-recurrent Transformers simulate complex computations?
method Hierarchical reparameterization of recurrent dynamics to simulate automata.
result Polynomial-sized, O(logT)-depth solutions exist and are common. This study compares fairness-enhancing machine learning techniques.
problem Disproportionate impact of machine learning predictions on different population subgroups.
method Developed an open benchmark to compare fairness-enhancing algorithms under various fairness measures and datasets.
result Different fairness-preserving algorithms tend to prioritize specific fairness measures, and their performance correlates strongly.
Unified framework for fair decision-making across diverse groups.
problem Statistical brittleness in fairness testing for small subgroups.
method Size-adaptive hypothesis testing framework.
result Validated approach for interpretable, statistically rigorous decisions.
Proposes a model combining order book data and herd behavior to replicate long-range memory in financial returns.
problem Replicating long-range memory in financial returns and trading activity.
method Combines empirical order book data and financial herd behavior model.
result Model successfully replicates long-range memory in absolute returns and trading activity.
Paper introduces adaptive parameterization to improve neural network efficiency.
problem Neural networks' limited flexibility due to fixed activation functions.
method Adaptive parameterization of feed-forward layers that learn to adapt based on input.
result Adaptive LSTM achieves state-of-the-art performance with fewer parameters and faster convergence.
Novel Bayesian neural network method for robustness.
problem Adversarial robustness without online training.
method Distributes uncertainty across all inputs.
result Demonstrates robustness on benchmark datasets.
Adversarial examples are due to non-robust features in data.
problem Understanding the reasons behind adversarial examples in machine learning.
method Developed a theoretical framework to identify non-robust features and demonstrated their widespread existence.
result Adversarial examples are a result of non-robust features in data.
New method calibrates models under covariate shifts.
problem Calibration of models can be lost under covariate shifts.
method Importance sampling based approach.
result Efficacy demonstrated on real-world and synthetic datasets.
Introduces PCG for better counterfactual explanations in vision models.
problem Ambiguity in latent-space optimization methods for counterfactual explanations.
method Constructs counterfactuals by tracing geodesics under a perceptually Riemannian metric.
result PCG outperforms baselines and reveals hidden failure modes.
Method learns Dirichlet-to-Neumann maps on graphs using Gaussian processes.
problem Coupling multiphysics simulations on graphs with conservation constraints.
method Gaussian processes combined with discrete exterior calculus and maximum likelihood estimation.
result Data-driven predictions with uncertainty quantification on entire graph.
Variational Proximal Policy Optimization improves reinforcement learning from human feedback.
problem Policy mode collapse and brittle exploration loops in reinforcement learning.
method Particle-based variational inference framework with Mixture-of-Experts architecture.
result Significant improvements in complex reasoning benchmarks.
Maximizes robustness in Bayesian experimental design under model uncertainty.
problem Brittleness of Bayesian experimental design under model misspecification.
method Formulates as a max--min game, uses Sibson's α-MI, and adopts PAC-Bayes framework.
result Establishes robust belief update and conditional information gain measure.
Better models make stochastic optimization more stable and robust.
problem Stability and robustness issues in standard stochastic optimization methods.
method Investigation of the aProx family of models for stochastic minimization and learning problems.
result Stochastic methods can be made stable, provably convergent, and asymptotically optimal with accurate models.
New natural environment benchmarks for RL to improve robustness.
problem Current RL benchmarks lack complexity and real-world applicability.
method Developed three new RL benchmark domains with natural complexity.
result Proposed benchmarks improve generalization and algorithm robustness.
Stabilizes deep Bayesian neural networks with self-stabilizing priors.
problem Brittleness and difficulty in training deep Bayesian neural networks.
method Signal propagation theory, reformulated ELBO, self-stabilizing priors.
result Improved convergence and robustness in training deeper networks and noisier settings.
A simple method treats heteroscedastic variance variatively, improving model calibration and sample quality.
problem Brittle optimization impacts model likelihoods for mean and variance estimation.
method Proposes a variational approach to heteroscedastic variance, improving predictive mean and variance calibration.
result The proposed method significantly improves parameter calibration and sample quality for regression and VAEs.
Continuum mechanics theory describes skin's complex anisotropic behavior.
problem Modeling the anisotropic tearing of skin.
method Finsler geometry fiber bundle approach, variational method, phase-field mechanics.
result Analytical solutions capture experimental data on skin tearing.