Generalization risk minimization in empirical game models

Development of a coupled genetic algorithm and empirical typhoon wind model and its application

In this paper, an operational Dst index prediction model is developed by combining empirical and Artificial Neural Network (ANN) models. ANN algorithms are widely used to predict space weather conditions. While they require a large amount of data for machine learning, large-scale geomagnetic storms have not occurred sufficiently for the last 20 years, Advanced Composition Explorer (ACE) and Deep Space Climate Observatory (DSCOVR) mission operation period. Conversely, the empirical models are based on numerical equations derived from human intuition and are therefore applicable to extrapolate for large storms. In this study, we distinguish between Coronal Mass Ejection (CME) driven and Corotating Interaction Region (CIR) driven storms, estimate the minimum Dst values, and derive an equation for describing the recovery phase. The combined Korea Astronomy and Space Science Institute (KASI) Dst Prediction (KDP) model achieved better performance contrasted to ANN model only. This model could be used practically for space weather operation by extending prediction time to 24 h and updating the model output every hour.

Identification of non-linear systems using empirical data and prior knowledge—an optimization approach

The complexity of state-of-the-art processor architectures and their consequent vast design spaces have made it difficult and time-consuming to explore the best configuration for them. Design space exploration (DSE) refers to systematic analysis and pruning of unwanted design points based on parameters of interest. DSE requires analysis and estimation of performance criteria of design points. A more accurate estimation produces a more efficient target design. A typical estimation method is machine learning approaches based on statistical inference, also known as empirical modeling, which requires only a limited number of simulations. Undoubtedly, an empirical model finds the optima much faster than using cycle-accurate simulations and is much more accurate than employing analytical models. For that purpose, our paper proposes a general methodology and a framework to find an appropriate and most accurate empirical model to estimate the performance of general-purpose or embedded multiprocessors running multithreaded workloads. This framework consists of three main steps: (1) Workload characterization and clustering, (2) Finding optimal model, and (3) Estimating the performance of a new workload outside the training set. These optimal performance prediction models could be utilized in the process of exploring the architectural design space. An experimental case is also tested using this framework for feasibility purposes. Validation experiments show MAEs less than 10% for this case.

Shear-waves are the most energetic body-waves radiated from an earthquake, and are responsible for the destruction of engineered structures. In both short-term emergency response and long-term risk forecasting of disaster-resilient built environment, it is critical to predict spatially accurate distribution of shear-wave amplitudes. Although decades’ old theory proposes a deterministic, highly anisotropic, four-lobed shear-wave radiation pattern, from lack of convincing evidence, most empirical ground-shaking prediction models settled for an oversimplified stochastic radiation pattern that is isotropic on average. Today, using the large datasets of uniformly processed seismograms from several strike, normal, reverse, and oblique-slip earthquakes across the globe, compiled specifically for engineering applications, we could reveal, quantify, and calibrate the frequency-, distance-, and style-of-faulting dependent transition of shear-wave radiation between a stochastic-isotropic and a deterministic-anisotropic phenomenon. Consequent recalibration of empirical ground-shaking models dramatically improved their predictions: with isodistant anisotropic variations of ±40%, and 8% reduction in uncertainty. The outcomes presented here can potentially trigger a reappraisal of several practical issues in engineering seismology, particularly in seismic ground-shaking studies and seismic hazard and risk assessment.

Optimized empirical model based on whale optimization algorithm for simulate daily reference crop evapotranspiration in different climatic regions of China

When facing a long-distance race, athletes and practitioners could develop an efficient pacing strategy and training paces if an accurate performance estimate of the target distance is achieved. Therefore, this study aims to determine the validity of different empirical models (i.e. critical power [CP], Power law and Peronnet) to predict long-duration power output (i.e. 60min) when using two or three time trial configurations. In a 5-week training period, fifteen highly trained athletes performed nine-time trials (i.e. 1, 2, 3, 4, 5, 10, 20, 30, and 60min) in a randomized order. Their power-duration curves were defined through the work-time (CPwork), power-1/time (CP1/time), two-parameter hyperbolic (CP2hyp), three-parameter hyperbolic (CP3hyp) CP models using different two- and three-time trial configurations. The undisclosed proprietary CP models of the Stryd (CPstryd) and Golden Cheetah training software (CPcheetah)were also computed as well as the non-asymptotic Power law and Peronnet models. These were extrapolated to the 60-min power output and compared to the actual performance. The shortest valid configuration (95% confidence interval < 12 W) for CPwork and CP1/time was 3-30min (Bias: 8.3 [4.9 to 11.7] W), for CPstryd was 10-30min (Bias: 4.2 [-1.0 to 9.4] W), for CP2hyp, CP3hyp and CPcheetah was 3-5-30min (Bias < 5.7 W), for Power law was 1-3-10min (-1.0 [-11.9 to 9.9] W), and for Peronnet was 4-20min (-3.0 [-10.2 to 4.3] W). All the empirical models provided valid estimates when the two or three predicting trial configurations selected attended each model fitting needs.

Water depth is important information for lake research. However, it is difficult to obtain the depth distribution of a whole lake. The method of remote-sensing inversion by combining bathymetric data has been proved to be feasible. In order to improve the inversion accuracy, it is very crucial to research the optimal inversion model and the most suitable depth for modelling, and the most appropriate satellite images for the study area. In this study, we used the three kinds of multispectral image data (Sentinel-2, GF-1, and Landsat-8) to establish models by combining bathymetric data in Bangda Co, which is located in the northwestern Tibetan Plateau. We verified the two empirical models (Stumpf model and Lyzenga model) with the three bands of blue, green, and red in 0–30 m water depth. We created a multi-factor combination model (S-L model) to compare with a BP neural network model using three images in five different water depth ranges (0–10, 0–15, 0–20, 0–25, and 0–30 m). The results indicated that the S-L model with mean absolute error (MAE) of 3.09–4.70 m, which the accuracy was slightly better than that of the two empirical models (3.35–5.03 and 3.23–4.94 m). The MAE of the BP model over the five depth ranges was 0.71–2.41 m for Landsat-8, 1.02–3.53 m for GF-1, and 1.15–3.68 m for Sentinel-2, which was higher than the S-L model. For the five ranges of three images by using the BP model, both the accuracy of Sentinel-2 and GF-1 in the range of 0–15 m was higher, but Landsat-8 was the highest at 0–20 m among five ranges; the mean relative errors (MRE) were 19.5%, 18.7%, 13.1%, and the root-mean-square error (RMSE) were 1.82, 1.72, and 1.86 m, respectively. The best applicability of the three images was Landsat-8, followed by GF-1 and Sentinel-2 in this study. The results suggested that the freely available Sentinel-2, GF-1 and Landsat-8 images could be used to estimate water storage for a shallow lake by combining bathymetric data on the Tibetan Plateau.

• Enhanced SSV airflow rate estimation of empirical models via data-driven modeling • Achieved high accuracy by customizing models individually for each room • Compared gray-box (physics-informed) with black-box (purely data-driven) models • Assessed data efficiency and model generalization using learning curve analysis • Revealed model robustness and key factors affecting airflow by sensitivity analysis Empirical models are widely used in building design and simulation to estimate airflow rates for single-sided natural ventilation (SSV). However, these models often suffer from limited accuracy. Data-driven modeling offers a viable solution to improve model accuracies by customizing models for various practical scenarios. This study investigates the feasibility and performance of two data-driven modeling approaches for SSV: gray-box modeling (physics-informed using polynomial regressions) and black-box modeling (purely data-driven using machine learning regressions). Taking SSV through a solitary bottom-hung window as a case study, training data were acquired from a full-scale test bench via tracer gas measurements conducted over 11 days. The results indicate that both modeling approaches significantly improve accuracy compared to existing empirical models from the literature. Notably, individual models tailored for each test room generally outperform generalized models developed for all rooms. This finding highlights the importance of customizing models on-site in real-world applications. When comparing the two modeling approaches, gray-box models require lower training ratios to achieve good generalization, demonstrating higher data efficiency than black-box models. Furthermore, sensitivity analysis reveals that gray-box models are more robust and reliable during interpolation and extrapolation, making them particularly suitable for scenarios with limited data availability. Incorporating opening area corrections and selecting root mean square error (RMSE) as the objective function are crucial for achieving optimal gray-box model performance. Overall, this study highlights the potential of physics-informed gray-box data-driven modeling as an advanced strategy for enhancing SSV airflow rate estimation, providing valuable insights to guide future research.

Abstract. Headwater streams represent a substantial proportion of river systems and many of them have intermittent flows due to their upstream position in the network. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The objective of this paper is to quantify how discrete (in space and time) field observations of flow intermittence help to extrapolate over time the daily probability of drying (defined at the regional scale). Two empirical models based on linear or logistic regressions have been developed to predict the daily probability of intermittence at the regional scale across France. Explanatory variables were derived from available daily discharge and groundwater-level data of a dense gauging/piezometer network, and models were calibrated using discrete series of field observations of flow intermittence. The robustness of the models was tested using an independent, dense regional dataset of intermittence observations and observations of the year 2017 excluded from the calibration. The resulting models were used to extrapolate the daily regional probability of drying in France: (i) over the period 2011–2017 to identify the regions most affected by flow intermittence; (ii) over the period 1989–2017, using a reduced input dataset, to analyse temporal variability of flow intermittence at the national level. The two empirical regression models performed equally well between 2011 and 2017. The accuracy of predictions depended on the number of continuous gauging/piezometer stations and intermittence observations available to calibrate the regressions. Regions with the highest performance were located in sedimentary plains, where the monitoring network was dense and where the regional probability of drying was the highest. Conversely, the worst performances were obtained in mountainous regions. Finally, temporal projections (1989–2016) suggested the highest probabilities of intermittence (&gt; 35 %) in 1989–1991, 2003 and 2005. A high density of intermittence observations improved the information provided by gauging stations and piezometers to extrapolate the temporal variability of intermittent rivers and ephemeral streams.

Highlights The frictional pressure drop correlation of agricultural residue-water slurry flows in vertical pipes is developed. Multiple linear regression with the backward elimination method was used in RStudio to obtain the optimal model. Some regression coefficients differ for different types of biomass feedstocks. The predicted pressure drops agree well with the experimental data within a 95% CI. Empirical models for the onset velocity of drag reduction of different particle sizes of biomass are proposed. Abstract. Large-scale biofuel production at levels equivalent to conventional oil refineries using long-distance pipeline hydro-transport of biomass can be a cleaner alternative to fossil fuels when it comes to economics and traffic congestion associated with the overland transportation of biomass. The transport of aqueous slurries of several saturated mass concentrations (5%-40%) and four particle sizes (from &amp;lt;3.2-19.2 mm) of two types of agricultural residue biomass (ARB) feedstock (corn stover and wheat straw) was studied through a vertical test section of a 29 m long, 50 mm diameter closed circuit pipeline facility, and frictional pressure drops were recorded at different flow rates (0.5-4.3 m s-1). A framework was developed in RStudio (4.0.5) to analyze the experimentally obtained frictional pressure drops of biomass slurries through a multiple linear regression approach using a backward elimination method and Akaike information criterion. An empirical model was proposed to predict slurry frictional pressure drop in terms of slurry velocity, slurry solid mass concentration, particle aspect ratio, and feedstock type. The model satisfactorily predicted the frictional pressure drops of both feedstocks of biomass-water slurry flows through pipes within a 95% confidence interval. The correlations introduced for onset velocities of drag reduction in terms of slurry solid mass concentrations seemed helpful to interpret the transition points of the corresponding slurries in vertical upward flows through pipes. The empirical correlation developed in this research could help select biomass slurry pumps and pipe dimensions when designing a typical long distance pipeline network for biofuel production at the commercial level. Keywords: Agricultural biomass wastes, Frictional loss prediction, Numerical model, Onset velocity correlation, Regression coefficients, Upward pipe flow.

The uncertainty of biomass estimates from LiDAR and SAR across a boreal forest structure gradient

The failure of orally administered glycoprotein IIb/IIIa inhibitors to prevent recurrent cardiac events

Researchers often use normal-form games to model multi-agent interactions. When a game model is based on observational or simulated data about agent payoffs, we call it an empirical game. The payoff matrix of an empirical game can be analyzed like any normal-form game, for example, by identifying Nash equilibria or instances of other solution concepts. Given the game model's basis in sampled data, however, empirical game analysis must also consider sampling error and distributional properties of candidate solutions. Toward this end, we introduce bootstrap techniques that support statistical reasoning as part of the empirical game-theoretic analysis process. First, we show how the bootstrap can be applied to compute confidence bounds on the regret of reported approximate equilibria. Second, we experimentally demonstrate that applying bootstrapped regret confidence intervals can improve sampling decisions in simulation-based game modeling.

Addressing the difficult problem of predicting the height of water-conducting fracture zones in shallow and thin coal seams, a prediction model of water-conduction fracture zones based on a backpropagation (BP) neural network was developed by integrating theoretical analysis, field measurements, and algorithmic advancements. Firstly, through overburden migration analysis and correlation tests, the height index system of the water-conducting fracture zone was determined. This system includes mining height, buried depth, dip angle, working face width, and overburden rock lithology, with five groups of characteristic parameters. Then, 35 pairs of minefield-measured data were collected to establish the measured height data set of the water-conducting fracture zone. Secondly, a BP neural network prediction model and a traditional support vector regression (SVR) prediction model were constructed based on a Pytorch framework, and the models were trained and tested by selecting data sets. Thirdly, the optimal prediction model was determined by comparing the model with the empirical model and multiple regression model of mining regulations for coal pillar maintenance and pressure in buildings, water bodies, railways, and main shafts. Finally, a typical mine was selected for application to verify the suitability of the optimal model. The results show that: (1) the predicted value of the neural network model is consistent with the change trend of the measured value, which accords with the theoretical law; (2) compared with traditional forecasting methods, the error of the BP neural network prediction model is stable and the prediction effect is the best; (3) dropout can effectively mitigate mitigation training overfitting, achieve regularization, and improve prediction accuracy; (4) the field application further verified that the BP neural network model is the best for predicting the height of water-conducting fracture zones of extremely thin coal seams, and the research results can provide technical guidance for similar fragile coal seams.