scholarly journals Data-Driven Modelling of Water Table Oscillations for a Porous Aquifer Occasionally Flowing under Pressure

Geosciences ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 282
Author(s):  
Angelo Doglioni ◽  
Vincenzo Simeone
Keyword(s):  
Neural Networks ◽  
Boundary Conditions ◽  
Water Table ◽  
Groundwater Resources ◽  
Physical Phenomenon ◽  
Measured Data ◽  
Data Driven ◽  
Low Permeability ◽  
Evolutionary Modeling ◽  
Porous Aquifer

Modelling of shallow porous aquifers in scenarios where boundary conditions change over time can be a difficult task. In particular, this is true when data modelling is pursued, i.e., models are directly constructed by measured data. In fact, data contain not only the information related to the physical phenomenon under investigation, but also the effects of time-varying boundary conditions, which work as a disturbance. This undesired component conditions the training of data-driven models, as they are fitted by models, which can produce predictions diverging from measured data. Here, a very shallow porous aquifer is modelled in terms of its response to water table to precipitation. The aquifer is characterized by the presence of a low permeability silty top layer covering the lower sandy strata, where the aquifer normally flows. Therefore, when the piezometric level increases up to the low permeability layer, the aquifer changes its behavior from phreatic to confined. This determines the changing boundary condition, which makes the response of the aquifer to rain precipitations complex, as it is related to a two-fold condition: confined or phreatic. The aquifer here is investigated by two machine learning approaches, the earlier based on an evolutionary modeling, and the latter based on artificial neural networks. Evolutionary modeling returned explicit equations with a fitness efficiency up to 0.8 for 1 month for predictions and 0.48 for simulations, while neural networks arrived at 0.85 and 0.28, respectively. The aim of this study is to get an explicit model of the response of the piezometric heights of the aquifer to the precipitations, which is useful for planning the use of groundwater resources.

Application of Visual MODFLOW to the Analysis of Boundary Conditions for a Phreatic Porous Aquifer Using Limited Available Information: A Case Study

2021 ◽  
Vol 44 ◽  
Author(s):  
Milena Stefany Lage Almeida ◽  
JOSÉ AUGUSTO COSTA GONÇALVES
Keyword(s):  
Boundary Conditions ◽  
Groundwater Flow ◽  
Root Mean Square ◽  
Flow Behavior ◽  
Groundwater Resources ◽  
Absolute Error ◽  
Mean Square ◽  
Visual Modflow ◽  
Porous Aquifer ◽  
The Mean

The increasing water demand, especially in developing regions, continuously puts pressure on groundwater resources both quantitatively and qualitatively. Hydrogeological modeling is a tool used in planning and management of groundwater resources. The factors that interfere in groundwater flow dynamics can be determined by developing a conceptual model and they can be validated via a numerical model. The objective of the manuscript is the hydrogeological groundwater flow modeling of the phreatic porous aquifer of the Ribeirão Candidópolis catchment in the Itabira municipality, State of Minas Gerais (Brazil). The software used in this study is GMS: MODFLOW, which enabled a steady state flow regime modeling by means of the Finite Difference Method (FDM) and the parameters calibration from a semi-transient approach. To assess the performance of the model, the Mean Error (ME), the Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE) were calculated. The results proved to be compatible with the values observed in the field. After several adjustments of the boundary conditions, a Normalized Root Mean Square (NRMS) of 9.648% and a correlation coefficient of 0.993 were obtained. Despite the economic importance of the study area, studies made available on groundwater flow behavior are rare. The results obtained via modeling are in accordance with the data observed in the field and consequently our model can be used in the study of water level changes.

scholarly journals Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

2021 ◽  
Vol 7 (15) ◽  
pp. eabe4166
Author(s):  
Philippe Schwaller ◽  
Benjamin Hoover ◽  
Jean-Louis Reymond ◽  
Hendrik Strobelt ◽  
Teodoro Laino
Keyword(s):  
Organic Chemistry ◽  
Neural Networks ◽  
Chemical Synthesis ◽  
Unsupervised Learning ◽  
Chemical Reactions ◽  
Data Driven ◽  
Experimental Task ◽  
Rule Based ◽  
Atom Mapping ◽  
Mapping Information

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

scholarly journals On the Experimental, Numerical and Data-Driven Methods to Study Urban Flows

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1310
Author(s):  
Pablo Torres ◽  
Soledad Le Clainche ◽  
Ricardo Vinuesa
Keyword(s):  
Physical Phenomenon ◽  
Urban Pollution ◽  
Pollutant Dispersion ◽  
Urban Environments ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Thermal Fields ◽  
Use Of Data ◽  
Sustainability Challenges ◽  
New Research

Understanding the flow in urban environments is an increasingly relevant problem due to its significant impact on air quality and thermal effects in cities worldwide. In this review we provide an overview of efforts based on experiments and simulations to gain insight into this complex physical phenomenon. We highlight the relevance of coherent structures in urban flows, which are responsible for the pollutant-dispersion and thermal fields in the city. We also suggest a more widespread use of data-driven methods to characterize flow structures as a way to further understand the dynamics of urban flows, with the aim of tackling the important sustainability challenges associated with them. Artificial intelligence and urban flows should be combined into a new research line, where classical data-driven tools and machine-learning algorithms can shed light on the physical mechanisms associated with urban pollution.

scholarly journals High accuracy data-driven heliostat calibration and state prediction with pretrained deep neural networks

Solar Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 48-56
Author(s):  
Max Pargmann ◽  
Daniel Maldonado Quinto ◽  
Peter Schwarzbözl ◽  
Robert Pitz-Paal
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
High Accuracy ◽  
Data Driven ◽  
State Prediction ◽  
Accuracy Data

scholarly journals Inverse design of glass structure with deep graph neural networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Qi Wang ◽  
Longfei Zhang
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Configuration Space ◽  
Glass Structure ◽  
Stable State ◽  
Specific Property ◽  
Inverse Design ◽  
Data Driven ◽  
Common Belief ◽  
Graph Neural Networks

AbstractDirectly manipulating the atomic structure to achieve a specific property is a long pursuit in the field of materials. However, hindered by the disordered, non-prototypical glass structure and the complex interplay between structure and property, such inverse design is dauntingly hard for glasses. Here, combining two cutting-edge techniques, graph neural networks and swap Monte Carlo, we develop a data-driven, property-oriented inverse design route that managed to improve the plastic resistance of Cu-Zr metallic glasses in a controllable way. Swap Monte Carlo, as a sampler, effectively explores the glass landscape, and graph neural networks, with high regression accuracy in predicting the plastic resistance, serves as a decider to guide the search in configuration space. Via an unconventional strengthening mechanism, a geometrically ultra-stable yet energetically meta-stable state is unraveled, contrary to the common belief that the higher the energy, the lower the plastic resistance. This demonstrates a vast configuration space that can be easily overlooked by conventional atomistic simulations. The data-driven techniques, structural search methods and optimization algorithms consolidate to form a toolbox, paving a new way to the design of glassy materials.

scholarly journals Identification of Granule Growth Regimes in High Shear Wet Granulation Processes Using a Physics-Constrained Neural Network

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 737
Author(s):  
Chaitanya Sampat ◽  
Rohit Ramachandran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Data Driven ◽  
Wet Granulation ◽  
Manufacturing Processes ◽  
High Shear ◽  
Process Data ◽  
Physical Constraints ◽  
Granule Growth

The digitization of manufacturing processes has led to an increase in the availability of process data, which has enabled the use of data-driven models to predict the outcomes of these manufacturing processes. Data-driven models are instantaneous in simulate and can provide real-time predictions but lack any governing physics within their framework. When process data deviates from original conditions, the predictions from these models may not agree with physical boundaries. In such cases, the use of first-principle-based models to predict process outcomes have proven to be effective but computationally inefficient and cannot be solved in real time. Thus, there remains a need to develop efficient data-driven models with a physical understanding about the process. In this work, we have demonstrate the addition of physics-based boundary conditions constraints to a neural network to improve its predictability for granule density and granule size distribution (GSD) for a high shear granulation process. The physics-constrained neural network (PCNN) was better at predicting granule growth regimes when compared to other neural networks with no physical constraints. When input data that violated physics-based boundaries was provided, the PCNN identified these points more accurately compared to other non-physics constrained neural networks, with an error of <1%. A sensitivity analysis of the PCNN to the input variables was also performed to understand individual effects on the final outputs.

Estimation of Site Amplification from Geotechnical Array Data Using Neural Networks

2021 ◽  
Author(s):  
Daniel Roten ◽  
Kim B. Olsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Site Response ◽  
Site Amplification ◽  
Data Driven ◽  
Vertical Array ◽  
The Neural Network ◽  
Simplifying Assumptions ◽  
The Mean ◽  
Fully Connected

ABSTRACT We use deep learning to predict surface-to-borehole Fourier amplification functions (AFs) from discretized shear-wave velocity profiles. Specifically, we train a fully connected neural network and a convolutional neural network using mean AFs observed at ∼600 KiK-net vertical array sites. Compared with predictions based on theoretical SH 1D amplifications, the neural network (NN) results in up to 50% reduction of the mean squared log error between predictions and observations at sites not used for training. In the future, NNs may lead to a purely data-driven prediction of site response that is independent of proxies or simplifying assumptions.

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