Hybridizes physical and data-driven methods for predicting physicochemical properties.
problem Predicting physicochemical properties accurately using limited data.
method Distills physical method predictions into a prior model and combines with sparse experimental data using Bayesian inference.
result Significant improvements in predicting activity coefficients at infinite dilution compared to baselines and ensemble methods.
The paper proposes a method to predict the performance of data-driven algorithms using surrogate models.
problem Improving the performance prediction of data-driven knowledge discovery algorithms.
method Surrogate-assisted performance prediction using evolutionary modeling of clinical pathways.
result The proposed approach provides interpretable prediction of algorithm performance and quality.
Study integrates machine learning with SAA for optimizing decisions based on uncertain parameters and covariates.
problem Optimizing decisions under uncertain parameters and covariates.
method Data-driven frameworks integrating machine learning prediction models within SAA for scenario generation.
result Consistent and asymptotically optimal solutions under certain conditions, with finite sample guarantees.
Kernel analog forecasting studied for multiscale systems.
problem Interpreting data-driven predictions in multiscale dynamical systems.
method Kernel analog forecasting methods applied to multiscale systems with varying Markovian closures.
result Guidance provided for interpreting data-driven predictions in practice.
Paper uses deep imitation learning to predict aircraft trajectories accurately.
problem Inefficient and costly Air Traffic Management system limits predictability.
method Generative Adversarial Imitation Learning framework with trajectory clustering and classification.
result Accurate predictions for entire trajectory stages, pre- and tactical.
A data-driven approach predicts morphological development under structural instability.
problem Understanding and predicting spatiotemporal complexities of morphogenesis under structural instability.
method Machine-learning framework based on physical modeling of morphogenesis.
result Identification of key bifurcation characteristics and prediction of history-dependent development.
New method uses FY loss for better inverse optimization.
problem Estimating unknown parameters from noisy and suboptimal solutions.
method Fenchel-Young loss approach for efficient gradient-based optimization.
result Significant improvement in parameter estimation accuracy and computational speed.
Study proposes a data-driven CBR system for improved bankruptcy prediction.
problem Lack of interpretability in machine learning models for bankruptcy prediction.
method Data-driven explainable case-based reasoning (CBR) system.
result Proposed CBR system outperforms existing CBR and machine learning models.
Paper presents a data-driven method for option pricing.
problem Option pricing accuracy under market volatility.
method Data-driven ensemble approach based on no-arbitrage theory.
result Model performance validated with real data.
With continuous glucose monitoring (CGM), data-driven models on blood glucose prediction have been shown to be effective in related work. However, such (CGM) systems are not always available, e.g., for a patient at home. In this work, we conduct a study on 9 patients and examine the online predictability of data-driven…
Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics community. Modern machine learning algorithms have opened up a new area of black-box turbulence models allowing for the tuning of RANS simulati…
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could also be used to predict global weather patterns days in advance. First studies show promise but the lack of a common dataset and evaluation me…
Unified framework for subgraph-enhanced GNNs, improving prediction accuracy and reducing computation time.
problem Limited understanding of subgraph-enhanced GNNs and their relation to the Weisfeiler-Leman hierarchy.
method Theoretical framework, theoretical expressivity results, and data-driven subgraph sampling methods.
result Data-driven subgraph-enhanced GNNs outperform non-data-driven methods in predictive performance.
A new framework for optimizing interventions with limited data.
problem Small data, default intervention data, unmodeled objectives, unforeseen consequences.
method Bandit data-driven optimization combining online bandit learning and offline predictive analytics.
result PROOF algorithm achieves no-regret and superior performance in simulations and real-world application.
CityTFT models urban building energy using a data-driven approach.
problem Current UBEM methods are time-consuming and based on physics.
method CityTFT uses a TFT framework with an augmented loss function.
result CityTFT predicts energy demands with high accuracy.
A new method for support vector regression using a data-driven insensitive parameter.
problem Determining an optimal insensitive parameter in support vector regression.
method A data-driven approach to approximate the insensitive parameter by minimizing a generalized loss function based on the likelihood principle.
result The proposed method outperforms traditional support vector regression methods and has lower computational costs.
Pipeline learns topological features for protein stability prediction.
problem Predicting protein stability using topological features.
method Data-driven method to learn topological features, comparing with expert features.
result Topological features achieve 92%-99% of SME-based models' performance.
Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisions because of the uncertainty in predictions arisen from insufficient quality and quantity of training data. Here, we show that Bayesian ne…
New methods quantify uncertainties in AI weather forecasts.
problem Uncertainty in AI weather predictions.
method Comparing ensemble and post-hoc uncertainty quantification methods.
result Probabilistic forecasts improve over ensemble physics-based models.
Bayesian neural networks improve uncertainty in data-driven VFMs for oil and gas wells.
problem Uncertainty and robustness in data-driven VFMs for oil and gas wells.
method Bayesian neural networks with variational inference for uncertainty quantification.
result Variational inference provides more robust predictions on future data.
New method combines neural nets with epidemic models for better prediction.
problem Improving epidemic prediction and forecasting using deep neural networks.
method Integrates machine learning with compartmental disease models for data-driven analysis.
result Data augmentation strategy improves neural network reliability for epidemic forecasting.
Paper introduces a new uncertainty measure for misclassification detection.
problem Effective detection of unreliable model predictions in machine learning.
method Data-driven measure of uncertainty relative to an observer based on soft-predictions.
result Demonstrates improved misclassification detection over state-of-the-art methods.
RCUKF combines data-driven modeling and Bayesian estimation for accurate system state estimation.
problem Challenges in obtaining reliable process models for complex systems.
method Integrates reservoir computing with unscented Kalman filtering.
result Demonstrated effectiveness on benchmark problems and real-time vehicle trajectory estimation.
Dividing local Gaussian processes improve real-time prediction efficiency.
problem Efficient online prediction for large data sets.
method Iterative data-driven division of input space for sublinear computational complexity.
result Sublinear computational complexity in real-time prediction.
Study merges datasets to improve AI model performance.
problem Improving machine learning models with diverse datasets.
method Developed an algorithm using oracle inequality and data-driven estimators.
result Algorithm reduces population loss with high probability.
This paper emphasizes the need for uncertainty quantification in data-driven ML models for nuclear engineering.
problem Uncertainty in ML predictions due to data noise, model architecture, and stochastic training.
method Explains and compares uncertainties in physics-based and data-driven models, and presents techniques to quantify ML prediction uncertainties.
result The importance of uncertainty quantification in ML models for nuclear engineering applications.
Air quality is closely related to public health. Health issues such as cardiovascular diseases and respiratory diseases, may have connection with long exposure to highly polluted environment. Therefore, accurate air quality forecasts are extremely important to those who are vulnerable. To estimate the variation of seve…
Deep learning improves solar energy forecasting using physical and data-driven models.
problem Improving short-term solar energy forecasting accuracy.
method Injecting physical knowledge into deep learning models for spatio-temporal forecasting.
result Improved solar energy forecasting models using deep learning and physical criteria.
Machine learning improves combustion system predictions by integrating physical models.
problem Improving accuracy of complex multi-physics systems like combustion.
method Coupling machine learning algorithms with physical models and constraints.
result Enhanced predictive capabilities in turbulent combustion.
New method recovers BSDE from financial data without ergodicity.
problem Discovering probabilistic laws from financial data.
method Stochastic SINDy method under risk-neutral measure.
result Recovery of BSDE from limited financial data.
Paper predicts embryo implantation probability from IVF time-lapse imaging.
problem Manual embryo selection in IVF has low success rates.
method Data-driven system trained on time-lapse imaging videos.
result Algorithm improves positive and negative predictive values.
We study data-driven assistants that provide congestion forecasts to users of shared facilities (roads, cafeterias, etc.), to support coordination between them, and increase efficiency of such collective systems. Key questions are: (1) when and how much can (accurate) predictions help for coordination, and (2) which as…
Improved weather forecasting using deep CNN on cubed-sphere grid.
problem Global weather prediction accuracy and speed.
method Deep convolutional neural network (CNN) on cubed-sphere grid, offline mapping, loss minimization.
result Significantly improved weather forecasts, indefinitely stable, realistic patterns at long lead times.
Proposes CPO framework for robust decision-making with explainable uncertainty regions.
problem Overly conservative uncertainty regions in data-driven optimization lead to suboptimal decisions.
method Conformal-Predict-Then-Optimize (CPO) framework using conditional generative models and visual summaries.
result Demonstrates improved robustness and explainability in decision-making.
Kernel methods accurately predict Hamiltonian systems from data.
problem Data-driven simulation of Hamiltonian systems.
method Two-step and one-step kernel-based methods for identifying and forecasting Hamiltonian systems.
result Framework achieves accurate, data-efficient predictions across various benchmark systems.
The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications. Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive. We introduce Union of Intersections (UoI), a flexible, m…
New method sparsifies hybrid neural ODEs for better performance and stability.
problem Excessive latent states and interactions from mechanistic models lead to training inefficiency and over-fitting.
method Automatic state selection and structure optimization combining domain-informed graph modifications with data-driven regularization.
result Improved predictive performance and robustness with desired sparsity.
Data scientists guide to streamflow prediction and flood forecasting.
problem Forecasting floods and predicting streamflow volume.
method Explains hydrologic concepts and machine learning applications.
result Helps data scientists understand streamflow prediction.
A new method improves the interpretability of data-driven models in ironmaking processes.
problem Lack of transparency in machine learning models used in industrial processes.
method Combines Variational Autoencoder (VAE) with Local Interpretable Model-agnostic Explanations (LIME) for better model interpretability.
result Improved local fidelity of local interpretable linear models compared to LIME.
Analyzes COVID-19 data to predict mortality, forecast spread, and optimize resource allocation.
problem Challenges in patient triage, treatment, and care management during the pandemic.
method Integrated four-step approach combining descriptive, predictive, and prescriptive analytics.
result Optimized resource allocation and informed policy decisions.
Slender marine structures such as deep-water marine risers are subjected to currents and will normally experience Vortex Induced Vibrations (VIV), which can cause fast accumulation of fatigue damage. The ocean current is often three-dimensional (3D), i.e., the direction and magnitude of the current vary throughout the …
We present a regression technique for data-driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate po…
Predicting the intraday stock jumps is a significant but challenging problem in finance. Due to the instantaneity and imperceptibility characteristics of intraday stock jumps, relevant studies on their predictability remain limited. This paper proposes a data-driven approach to predict intraday stock jumps using the in…
PhysVarMix predicts diverse urban trajectories with physics constraints.
problem Predicting complex urban agent trajectories with multiple plausible scenarios.
method Physics-informed variational mixture model combining learning and physics constraints.
result Superior performance compared to existing methods on benchmark datasets.
With the advent of modern data collection and storage technologies, data-driven approaches have been developed for discovering the governing partial differential equations (PDE) of physical problems. However, in the extant works the model parameters in the equations are either assumed to be known or have a linear depen…
We examine counterfactual explanations for explaining the decisions made by model-based AI systems. The counterfactual approach we consider defines an explanation as a set of the system's data inputs that causally drives the decision (i.e., changing the inputs in the set changes the decision) and is irreducible (i.e., …
Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, an…
Paper proposes a dual-level approach for multi-step forecasting of dynamical systems.
problem Accurate multi-step forecasting of time series systems for automatic control and optimization.
method Hybrid input forecasting using LSTM-STMs and physics-informed neural networks (PINNs).
result Hybrid models achieve higher log-likelihood and lower MSE compared to conventional methods.