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218 results for “Physical Modelling”
MATHEMATICAL AND PHYSICAL MODELING FORECASTING IN MEDICINE
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Coupling deep learning and physically-based hydrological models for monthly streamflow predictions
<p>Revision in journal Water Resources Research, Paper # <strong><span>2023WR035618R</span></strong></p>
Part of the dataset and trained models in "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media" by Meng et al. in Geophysical Research Letters
<p><strong>This repository is created to contain part of the data, codes and trained models in the research project titled "A Physics-Enhanced Neural Network for Estimating Longitudinal Dispersion Coefficient and Average Solute Transport Velocity in Porous Media".</strong></p> <p> </p>
A physical model for mean river discharge calculation: from riverside seismic monitoring experiments in a low-flow river, China
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Feasibility of a Randomized Controlled Trial of Large Artificial Intelligence-Based Linguistic Models for Clinical Reasoning Training of Physical Therapy Students.
<p>Data collected for students who participated in the study</p>
The hydrological fluxes of the Upper Brahmaputra River Basin constrained by a multi-physics ensemble (MPE) modeling approach
<p>The data represent monthly hydrological fluxes for four sub-basins within the Upper Brahmaputra River Basin, where yyyy is the year, mm is the month, MPE is the multi-physics ensemble, P is the precipitation, R is the runoff, and ET is the evapotranspiration. The unit is mm. The upper-bounds and lower-bounds represent the upper and lower bounds of the hydrological fluxes constrained by the MPE, respectively.</p> <p> </p> <p>Reference:<br>Lei, X, P. Lin*, H. Zheng, K. Yang, W. Liu, C. Miao, K. Wang, J. Wang: A multi-physics ensemble modeling approach to constraining the uncertainty of hydrological fluxes in sparsely-gauged river basins. Geophysical Research Letters, (submitted), 2024.<br>Contact:<br>xiangyonglei@stu.pku.edu.cn; peironglinlin@pku.edu.cn</p>
Integrated Full-Scale Physical Experiments and Numerical Modeling of the Performance and Rehabilitation of Highway Embankments
<p>Corresponding data set for Tran-SET Project No. 18GTLSU06. Abstract of the final report is stated below for reference:</p> <p>"The study aimed to fundamentally understand how soil strength and hydraulic properties are impacted by recurring cycles of wetting and drying induced by climate variability, with the practical implication of forecasting the stability of highway embankment slopes. The review of literature on the effects of long-term cyclic wetting-drying phases on hydro-mechanical properties of clayey soils suggests that only a few cycles of wetting and drying can impact the strength and hydraulic conductivity, where the latter can increase several orders of magnitude. Laboratory model-scale experiments of Louisiana and Texas soils are still ongoing and will relate the laboratory test results to weathering cycles by accounting for parameters such as rainfall intensity and duration, evapotranspiration, temperature, and relative humidity. The objective of the laboratory testing was to determine the strength and unsaturated soil properties of samples collected from laboratory model-scale experiments and to investigate the subsequent changes in the hydro-mechanical properties of those clayey soils. For example, shrinkage test results provided a measure of the propensity and extent of strength loss incurred by a soil specimen when exposed to weathering cycles. Numerical modeling of highway embankments with material properties and test results obtained from lab testing were used to predict the factor of safety for an embankment in Texas."</p>
A Controlled-Source Physical Model for Long Period Events
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Insights on SEI Growth and Properties in Na-Ion Batteries via Physically Driven Kinetic Monte Carlo Model
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Data from: Using hidden Markov models to improve quantifying physical activity in accelerometer data – a simulation study
Introduction The use of accelerometers to objectively measure physical activity (PA) has become the most preferred method of choice in recent years. Traditionally, cutpoints are used to assign impulse counts recorded by the devices to sedentary and activity ranges. Here, hidden Markov models (HMM) are used to improve the cutpoint method to achieve a more accurate identification of the sequence of modes of PA. Methods:1,000 days of labeled accelerometer data have been simulated. For the simulated data the actual sedentary behavior and activity range of each count is known. The cutpoint method is compared with HMMs based on the Poisson distribution (HMM[Pois]), the generalized Poisson distribution (HMM[GenPois]) and the Gaussian distribution (HMM[Gauss]) with regard to misclassification rate (MCR), bout detection, detection of the number of activities performed during the day and runtime. Results:The cutpoint method had a misclassification rate (MCR) of 11% followed by HMM[Pois] with 8%, HMM[GenPois] with 3% and HMM[Gauss] having the best MCR with less than 2%. HMM[Gauss] detected the correct number of bouts in 12.8% of the days, HMM[GenPois] in 16.1%, HMM[Pois] and the cutpoint method in none. HMM[GenPois] identified the correct number of activities in 61.3% of the days, whereas HMM[Gauss] only in 26.8%. HMM[Pois] did not identify the correct number at all and seemed to overestimate the number of activities. Runtime varied between 0.01 seconds (cutpoint), 2.0 minutes (HMM[Gauss]) and 14.2 minutes (HMM[GenPois]). Conclusions: Using simulated data, HMM-based methods were superior in activity classification when compared to the traditional cutpoint method and seem to be appropriate to model accelerometer data. Of the HMM-based methods, HMM[Gauss] seemed to be the most appropriate choice to assess real-life accelerometer data.
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
<p><strong>Abstract:</strong><br> To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC estimates over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. We further show that our MC estimates correctly match the distribution of ground-truth observations, thus making the PGA paradigm amenable to physically grounded uncertainty quantification.</p>
Supplementary data: "Physics-informed machine learning for power grid frequency modelling"
<p>This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" <a href="https://doi.org/10.48550/arXiv.2211.01481">(Preprint)</a>. The code for producing the processed data and the results is <a href="https://github.com/johkruse/PIML-for-grid-frequency-modelling">available at github</a>.</p> <p><strong>Results</strong></p> <p>The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files:</p> <ul> <li><em>tuning </em>: Results of hyper-parameter tuning.</li> <li><em>best_model </em>: Weights of the trained model with best hyper-parameters.</li> <li><em>best_model_<scaling-variation> </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling.</li> <li><em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised.</li> <li><em>shap_values_<parameter>_long.h5</em> : SHAP values for the prediction of the system parameters.</li> </ul>
Closing in on Hydrologic Predictive Accuracy: Combining the Strengths of High-Fidelity and Physics-Agnostic Models
<p>The zip file contains a synthetic dataset that was used to construct the surrogate model.</p>
A New Rock Physics Model for Predicting the Elastic Properties of Sediments Hosting Nodule and Chunk-like Natural Gas Hydrate Morphologies
<p>MATLAB codes and well logs used in the manuscript are included.</p>
Biopsychosocial Model-based Care Versus Routine Physical Therapy in Chronic Back Pain
ClinicalTrials.gov study NCT07280806. IPD Sharing: YES. Countries: 1. Publications: 0.
Supervised Walking Groups as a Model to Increase Physical Activity in Type 2 Diabetes
ClinicalTrials.gov study NCT01115205. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Active Transport Educational Program Based on the Ecological Model on Improving the Physical and Mental Health: MOV-ES Project
ClinicalTrials.gov study NCT06357065. IPD Sharing: NO. Countries: 0. Publications: 53.
Data from: Modelling verbal aggression, physical aggression and inappropriate sexual behaviour after brain injury
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Data from: Collector motion affects particle capture in physical models and in wind pollination
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Data from: Modeling human population separation history using physically phased genomes
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ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.