Short- and long-term predictions of chaotic flows and extreme events: a physics-constrained reservoir computing approach

We propose a physics-constrained machine learning method—based on reservoir computing—to time-accurately predict extreme events and long-term velocity statistics in a model of chaotic flow. The method leverages the strengths of two different approaches: empirical modelling based on reservoir computing, which learns the chaotic dynamics from data only, and physical modelling based on conservation laws. This enables the reservoir computing framework to output physical predictions when training data are unavailable. We show that the combination of the two approaches is able to accurately reproduce the velocity statistics, and to predict the occurrence and amplitude of extreme events in a model of self-sustaining process in turbulence. In this flow, the extreme events are abrupt transitions from turbulent to quasi-laminar states, which are deterministic phenomena that cannot be traditionally predicted because of chaos. Furthermore, the physics-constrained machine learning method is shown to be robust with respect to noise. This work opens up new possibilities for synergistically enhancing data-driven methods with physical knowledge for the time-accurate prediction of chaotic flows.

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Antibody Complementarity Determining Region Design Using High-Capacity Machine Learning

10.1101/682880 ◽

2019 ◽

Cited By ~ 1

Author(s):

Ge Liu ◽

Haoyang Zeng ◽

Jonas Mueller ◽

Brandon Carter ◽

Ziheng Wang ◽

...

Keyword(s):

Machine Learning ◽

Structural Information ◽

High Capacity ◽

Training Data ◽

Proper Function ◽

Integrative Approach ◽

Machine Learning Method ◽

Learning Method ◽

Target Specificity

AbstractThe precise targeting of antibodies and other protein therapeutics is required for their proper function and the elimination of deleterious off-target effects. Often the molecular structure of a therapeutic target is unknown and randomized methods are used to design antibodies without a model that relates antibody sequence to desired properties. Here we present a machine learning method that can design human Immunoglobulin G (IgG) antibodies with target affinities that are superior to candidates from phage display panning experiments within a limited design budget. We also demonstrate that machine learning can improve target-specificity by the modular composition of models from different experimental campaigns, enabling a new integrative approach to improving target specificity. Our results suggest a new path for the discovery of therapeutic molecules by demonstrating that predictive and differentiable models of antibody binding can be learned from high-throughput experimental data without the need for target structural data.SignificanceAntibody based therapeutics must meet both affinity and specificity metrics, and existing in vitro methods for meeting these metrics are based upon randomization and empirical testing. We demonstrate that with sufficient target-specific training data machine learning can suggest novel antibody variable domain sequences that are superior to those observed during training. Our machine learning method does not require any target structural information. We further show that data from disparate antibody campaigns can be combined by machine learning to improve antibody specificity.

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Estimations of Long-Term nss-SO42– and NO3– Wet Depositions over East Asia by Use of Ensemble Machine-Learning Method

Environmental Science & Technology ◽

10.1021/acs.est.0c01068 ◽

2020 ◽

Vol 54 (18) ◽

pp. 11118-11126

Author(s):

Xingcheng Lu ◽

Dehao Yuan ◽

Yiang Chen ◽

Jimmy C.H. Fung ◽

Wenkai Li ◽

...

Keyword(s):

Machine Learning ◽

East Asia ◽

Machine Learning Method ◽

Learning Method ◽

Ensemble Machine Learning

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Bare-earth DEM Generation in Urban Areas Based on a Machine Learning Method

10.5194/egusphere-egu2020-13215 ◽

2020 ◽

Author(s):

Yinxue Liu ◽

Paul Bates ◽

Jeffery Neal ◽

Dai Yamazaki

Keyword(s):

Machine Learning ◽

Urban Areas ◽

Rapid Development ◽

Training Data ◽

Acquisition Time ◽

World Population ◽

Machine Learning Method ◽

Learning Method ◽

Night Time ◽

Inundation Depth

<p>Precise representation of global terrain is of great significance for estimating global flood risk. As the most vulnerable areas to flooding, urban areas need GDEMs of high quality. However, current Global Digital Elevation Models (GDEMs) are all Digital Surface Models (DSMs) in urban areas, which will cause substantial blockages of flow pathways within flood inundation models. By taking GPS and LIDAR data as terrain observations, errors of popular GDEMs (including SRTM 1&#8221; void-filled version DEM - SRTM, Multi-Error-Removed Improved-Terrain DEM - MERIT and TanDEM-X 3&#8221; resolution DEM -TDM3) were analysed in seven varied types of cities. It was found that the RMSE of GDEMs errors are in the range of 2.3 m &#8211; 7.9 m, and that MERIT and TDM3 both outperformed SRTM. The error comparison between MERIT and TDM3 showed that the most accurate model varied among the studied cities. Generally, error of TDM3 is slightly lower than MERIT, but TDM3 has more extreme errors (absolute value exceeds 15 m). For cities which have experienced rapid development in the past decade, the RMSE of MERIT is lower than that of TDM3, which is mainly caused by the acquisition time difference between these two models. A machine learning method was adopted to estimate MERIT error. Night Time Light, world population density data, Openstreetmap building data, slope, elevation and neighbourhood elevation values from widely available datasets, comprising 14 factors in total, were used in the regression. Models were trained based on single city and combinations of cities, respectively, and then used to estimate error in a target city. By this approach, the RMSE of corrected MERIT can decline by up to 75% with target city trained model, though less significant a reduction of 35% -68% was shown in the combined model with target city excluded in the training data. Further validation via flood simulation showed improvements in terms of both flood extent and inundation depth by the corrected MERIT over the original MERIT, with a validation in small sized city. However, the corrected MERIT was not as good as TDM3 in this case. This method has the potential to generate a better bare-earth global DEM in urban areas, but the sensitive level about the model extrapolative application needs investigation in more study sites.</p>

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