kriging
PulseAugur coverage of kriging — every cluster mentioning kriging across labs, papers, and developer communities, ranked by signal.
6 day(s) with sentiment data
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Gaussian Process Regression Enhances Monte Carlo Tree Search for Continuous Actions
Researchers have developed a new method for Monte Carlo Tree Search (MCTS) that utilizes Gaussian Process Regression to improve performance in environments with continuous action spaces. This approach aims to better agg…
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New Gaussian Process method solves complex wave problems with uncertainty quantification
Researchers have developed a novel method for solving complex wave propagation problems governed by the Helmholtz equation, particularly in dissipative media where the squared wavenumber is complex. This new approach ex…
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Machine learning boosts wind power forecast accuracy
Researchers have developed advanced machine learning techniques to improve wind power forecasting accuracy. A comparative analysis of conformalized quantile regression, natural gradient boosting, and conditional diffusi…
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Machine learning model outperforms physics-based simulation for hydraulic clutch control
This paper introduces a data-driven method for modeling hydraulic clutch control pressure, addressing nonlinear behaviors like hysteresis and latch transitions. By incorporating current derivative information and using …
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New SHARC framework enhances explainability for ML risk models in finance
A new research paper introduces SHARC, an explainability framework designed for machine learning risk models used in regulatory capital estimation. SHARC addresses the 'black box' problem by applying SHapley Additive ex…
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New data-driven models predict pressure losses in turbulent flows
Researchers have developed two new data-driven models, one using kriging and the other a neural network (NN), to predict pressure losses in turbulent flows across perforated plates. These models were trained on experime…
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New GAIA framework enhances LLM instruction tuning with global data selection
Researchers have developed GAIA (Global Adaptive Instruction tuning via Gaussian processes), a novel framework for selecting high-quality data for Large Language Model (LLM) instruction tuning. Unlike existing methods t…
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New kriging and neural network models predict pressure losses
Researchers have developed two new data-driven models, one using kriging and another employing artificial neural networks (NN), to predict pressure losses in turbulent flows across perforated plates. These models were t…
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New framework for nonlinear system identification introduced
Researchers have introduced Orthogonal Discrepancy Kernels (ODKs), a novel semi-parametric framework designed for nonlinear system identification. This approach effectively separates discrepancy functions from physics-b…
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New F2NARX model offers significant efficiency and accuracy gains for dynamical systems
Researchers have introduced a new Function-on-Function Nonlinear AutoRegressive model with eXogenous inputs (F2NARX), which enhances predictive efficiency and accuracy for complex dynamical systems. This novel framework…
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New AI Framework Enhances Control for Multi-Fuel Engines
Researchers have developed a new data-driven control framework for multi-fuel compression ignition (CI) engines to address challenges in achieving consistent combustion phasing. The system utilizes Gaussian Process Regr…
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AI reconstructs temperature fields using simulated data
Researchers have developed a novel method for generating synthetic datasets using physics-based simulations to train neural networks for reconstructing unobservable temperature fields. This simulation-aided intelligent …
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New Gaussian process method reconstructs fluid flow fields
Researchers have developed a novel method for reconstructing fluid flow fields using physics-informed Gaussian process regression. This technique incorporates boundary constraints directly into the regression process, a…
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CNNs Offer New Approach to Spatial Interpolation
Researchers have developed a novel approach to spatial interpolation using convolutional neural networks (CNNs). This method trains on a single, partially observed field to predict values at unobserved locations, bypass…
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Multi-Agent RL Maps River Plumes Efficiently
Researchers have developed a novel multi-agent reinforcement learning approach for long-term mapping of river plumes, specifically demonstrated using the Douro River. This method employs a central coordinator that inter…
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Paper: Transformers can learn distributions in-context
A new paper explores the theoretical capabilities of transformers in learning distributions within context, specifically focusing on Bayesian prediction tasks. Researchers demonstrate how transformers can implement grad…
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New method offers tight uncertainty bounds for kernel regression
Researchers have developed a new method for calculating tight, deterministic uncertainty bounds for multivariate functions within Reproducing Kernel Hilbert Spaces. This approach is designed to work under bounded noise …
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New physics-constrained GPR improves structural mode shape reconstruction
Researchers have developed a new Physics-Constrained Gaussian Process Regression (CONS-SOGP) framework to improve the reconstruction of structural mode shapes from limited sensor data. This method addresses inconsistenc…