SGNNs use simulations to train neural networks, improving scientific forecasting and interpretability.
problem Combining precise theory and machine learning for robust scientific modeling.
method Pretraining neural networks on diverse mechanistic simulations as training data.
result SGNNs outperform data-driven and physics-constrained models in forecasting and interpretability.
Study uses machine learning to predict predator-prey dynamics without prior knowledge.
problem Predicting predator-prey interactions without prior knowledge of the system.
method Applied Neural Ordinary Differential Equations (Neural ODEs) and Universal Differential Equations (UDEs) to the Lotka-Volterra model.
result UDEs outperform Neural ODEs in predicting predator-prey dynamics, especially in noisy data.
A new method calibrates scientific models by adding randomness to their predictions.
problem Current scientific foundation models lack calibrated uncertainty.
method Stochastic Attention, which randomizes attention weights using multinomial samples.
result Stochastic Attention achieves the strongest native calibration and sharpest prediction intervals.
HierarchicalForecast provides a Python framework for coherent hierarchical forecasting.
problem Ensuring forecasts at disaggregate levels add up to aggregate forecasts.
method Preprocessed datasets, evaluation metrics, and statistical baseline models.
result Python-based reference framework for statistical and ML forecasting.
DecompKAN improves time series forecasting accuracy and transparency.
problem Accurate and transparent time series forecasting in scientific domains.
method Combines decomposition, patching, normalization, and B-spline KAN edge functions.
result Achieves best or tied-best MSE on 20 of 36 comparisons across 9 datasets.
In this paper, we present a new approach to time series forecasting. Time series data are prevalent in many scientific and engineering disciplines. Time series forecasting is a crucial task in modeling time series data, and is an important area of machine learning. In this work we developed a novel method that employs …
AI helps forecasters understand TC convective evolution before intensification.
problem Challenges in extracting scientific insights from complex TC data.
method Combining AI prediction algorithms and classical statistical inference.
result Identifies patterns in TC convective structure leading to intensification.
The paper introduces a fast algorithm for learning and forecasting nonlinear dynamics from noisy time series data.
problem Challenges in capturing nonlinear dynamics from noisy time series data.
method A projected nonlinear state-space model with kernel functions applied to projected lines.
result The model effectively learns and forecasts complex nonlinear dynamics with computational efficiency.
ARHNN method improves electricity price forecasting accuracy.
problem Improving accuracy in electricity price forecasting.
method Combines Autoregressive Hybrid Nearest Neighbors (ARHNN) method with calibration sample selection and forecast combination.
result ARHNN method outperforms benchmarks by up to 10% in German, Spanish, and New England markets.
SVGP KAN integrates uncertainty quantification into Kolmogorov-Arnold networks.
problem Uncertainty quantification in scientific machine learning models.
method Sparse variational Gaussian process inference with Kolmogorov-Arnold topology.
result Demonstrated ability to distinguish aleatoric and epistemic uncertainty in various scientific applications.
MetNet forecasts precipitation up to 8 hours with high spatial and temporal resolution.
problem Precise weather forecasting for long lead times.
method Neural network architecture using axial self-attention for global context aggregation.
result MetNet outperforms Numerical Weather Prediction at forecasts of up to 8 hours.
Proposes a graph neural network for traffic forecasting in WANs.
problem Traffic forecasting challenges in WANs due to dynamic and large data volumes.
method Dynamic diffusion convolutional recurrent neural networks for multistep traffic forecasting.
result Significant improvements in forecasting accuracy compared to classical methods.
In many scientific fields, such as economics and neuroscience, we are often faced with nonstationary time series, and concerned with both finding causal relations and forecasting the values of variables of interest, both of which are particularly challenging in such nonstationary environments. In this paper, we study c…
EventFlow forecasts event sequences without autoregression, improving accuracy.
problem Forecasting errors in autoregressive models for event sequences.
method EventFlow uses flow matching to learn joint distributions over event times directly.
result EventFlow reduces forecast error by 20%-53% compared to baselines.
CauSTream forecasts streamflow by integrating causal graphs for better interpretability.
problem Streamflow forecasting lacks interpretability and generalization due to fixed causal models.
method CauSTream learns causal graphs for meteorological forcings and routing dependencies.
result CauSTream outperforms existing methods, especially at longer forecast windows.
Paper analyzes cyber risk classifications for forecasting performance.
problem Lack of effective out-of-sample forecasting performance in current cyber risk classifications.
method Rolling window analysis using threshold weighted scoring functions.
result Dynamic and impact-based cyber risk classifiers outperform others in forecasting future cyber risk losses.
WeatherBench provides a dataset and metrics for comparing data-driven weather forecasts.
problem Lack of a common dataset and evaluation metrics for data-driven weather forecasting.
method Publicly available dataset derived from ERA5, simple evaluation metrics.
result Baseline scores from various forecasting methods provided for comparison.
This work shows how evaluation metrics can be seen as fair gambles.
problem The relationship and evaluation of machine learning forecasts.
method Using game-theoretic probability, the authors show evaluation metrics as fair gambles.
result Standard evaluation metrics are fair gambler outcomes, with calibration and regret metrics on two dimensions.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
Improved time series forecasting with multivariate probabilistic models.
problem Improving accuracy in forecasting time series with statistical dependencies.
method Conditioned Normalizing Flows for autoregressive deep learning models.
result Improved performance over state-of-the-art models on real-world data sets.
This paper analyzes machine learning workflows in climate modeling.
problem Challenges in integrating machine learning with climate modeling.
method Analysis of case studies focusing on design patterns and workflow structure.
result Synthesis of workflow design patterns across diverse projects in ML-enabled climate modeling.
Project forecasts crop prices to help Indian farmers choose optimal crops for better ROI.
problem Indian farmers struggle to choose crops that fetch decent profits due to lack of scientific decision-making.
method Price forecasting to create an optimal portfolio of crops for better ROI.
result Data-driven decision-making for crop selection leads to higher estimated ROI.
Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road …
LAVARNET predicts multivariate time series by estimating causal variable relationships.
problem Forecasting multivariate time series requires understanding causal interrelationships among variables.
method LAVARNET is a neural network architecture that estimates causal effects and predicts future values.
result LAVARNET outperforms other models on various real-world data sets.
Framework augments physical models with deep learning for complex dynamics forecasting.
problem Forecasting complex dynamical phenomena with partial knowledge.
method APHYNITY framework: decomposes dynamics into physical and data-driven components.
result Framework accurately forecasts system evolution and identifies relevant parameters.
DAISI improves data assimilation for complex systems with noisy observations.
problem Limited accuracy of classical DA methods in complex, nonlinear systems.
method Generative models with inverse sampling for flexible probabilistic inference.
result DAISI achieves accurate filtering results in challenging nonlinear systems.
New analysis shows FM learns underlying dynamical structure, not just trajectory replay.
problem Understanding whether flow matching models learn transferable dynamical structure or merely replay trajectories.
method Derived velocity field implied by FM objective, characterized as a continuous-time dynamical system.
result FM models can be seen as parametric surrogates of nonparametric solutions, providing strong probabilistic forecasts.
Novel framework discovers SPDEs from limited data.
problem Discovering SPDEs from limited data.
method Combines stochastic calculus, variational Bayes, and sparse learning.
result Accurately identifies SPDEs from limited data.
Spatio-temporal data and processes are prevalent across a wide variety of scientific disciplines. These processes are often characterized by nonlinear time dynamics that include interactions across multiple scales of spatial and temporal variability. The data sets associated with many of these processes are increasing …
New method improves spatial prediction validation accuracy.
problem Validation methods fail for spatial prediction tasks due to mismatch between validation and test locations.
method Proposes a new validation method that adapts existing covariate-shift ideas to spatial settings.
result Proves and demonstrates the new method's superiority in spatial prediction validation.
High levels of air pollution may seriously affect people's living environment and even endanger their lives. In order to reduce air pollution concentrations, and warn the public before the occurrence of hazardous air pollutants, it is urgent to design an accurate and reliable air pollutant forecasting model. However, m…
In statistical analysis, measuring a score of predictive performance is an important task. In many scientific fields, appropriate scores were tailored to tackle the problems at hand. A proper score is a popular tool to obtain statistically consistent forecasts. Furthermore, a mathematical characterization of the proper…
xVal tokenizes numbers continuously for better scientific model training.
problem Lack of continuous numerical tokenization for scientific datasets in LLMs.
method xVal: Continuous numerical tokenization strategy.
result xVal outperforms other numerical tokenization methods on scientific datasets.
Galactica learns from scientific literature to help researchers.
problem Information overload in scientific literature makes it hard to find useful insights.
method Trained on a large corpus of scientific papers, reference material, and knowledge bases.
result Outperforms existing models on various scientific tasks, including LaTeX equations and mathematical reasoning.
DeepVARMA predicts chemical industry index trends using LSTM and VARMAX models.
problem Forecasting the chemical industry index for economic analysis.
method Combines LSTM and VARMAX models to predict nonstationary series.
result DeepVARMA achieves best prediction accuracy and adaptability.
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of da…
Social media enhances or diminishes scientific status, depending on usage.
problem Impact of social media on scientific stratification and mobility.
method Logistic Attribution Analysis combining statistical and machine learning methods.
result Social media promotes stratification and mobility, but beyond a threshold, it negatively impacts status.
This paper reviews causal inference methods for time series data.
problem Estimating treatment effects and identifying causal relations from time series data.
method Comprehensive review of approaches for treatment effect estimation and causal discovery.
result Provides a list of evaluation metrics and datasets for time series causal inference.
Algorithm optimizes electricity procurement costs by 1.65%.
problem Minimizing energy cost while covering forecast consumption.
method Deep learning forecasting and deviation indicator.
result Reduction of 1.65% in costs compared to uniform policy.
Evidence shows that in a significant number of cases the current methods of research do not allow for reproducible and falsifiable procedures of scientific investigation. As a consequence, the majority of critical decisions at all levels, from personal investment choices to overreaching global policies, rely on some va…
Accurate real time crime prediction is a fundamental issue for public safety, but remains a challenging problem for the scientific community. Crime occurrences depend on many complex factors. Compared to many predictable events, crime is sparse. At different spatio-temporal scales, crime distributions display dramatica…
There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the scientific plausibility or validity of outputs predicted by a neural network. This w…
Paper proposes a method to estimate scientific parameters in hybrid models without relying on model architecture.
problem Estimating unknown parameters in hybrid models combining machine learning and scientific models.
method Sharpness-aware minimization adapted for hybrid modeling, focusing on model simplicity.
result Demonstrates effectiveness of SAM-based hybrid model learning for scientific parameter estimation.
AutoSciDACT detects scientific anomalies in noisy data.
problem Detecting anomalies in large, noisy scientific datasets.
method Contrastive pre-training for low-dimensional data representations, two-sample test using NPLM.
result Strong sensitivity to small anomalies across various scientific domains.
MDNs offer a data-efficient alternative to diffusion and flow models for multimodal scientific learning.
problem Capturing multimodal conditional uncertainty in scientific inverse problems.
method Mixture Density Networks (MDNs) as explicit parametric density estimators.
result MDNs achieve superior generalization, interpretability, and sample efficiency in scientific tasks.
New architectures improve KANs, making them more interpretable and accurate.
problem Improving Kolmogorov-Arnold networks while maintaining interpretability.
method Overprovisioned architectures combined with sparsification, deep supervision, and depth selection, optimized with a minimum description length objective.
result Combining sparsification with depth selection achieves competitive or superior accuracy while discovering smaller models.
Survey of deep learning models for scientific discovery.
problem Identifying which scientific problems are most suitable for deep learning.
method Overview of deep learning models, tasks, training methods, and techniques.
result Helps accelerate deep learning use in scientific domains.
New framework compresses and recovers scientific data efficiently.
problem Efficiently managing and recovering from large scientific datasets.
method Grounded in learning exponential families, preserves uncertainty and supports trade-offs.
result Preserves physical features and quantities of interest in compressed representations.