pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning Based Cloud Cover Parameterization for ICON
Authors: Arthur Grundner, Tom Beucler, Pierre Gentine, Fernando Iglesias-Suarez, Marco A. Giorgetta, Veronika Eyring
Date: 2021-12-21 00:00:00
Tasks: Deep Learning, Feature Importance

A promising approach to improve cloud parameterizations within climate models and thus climate projections is to use deep learning in combination with training data from storm-resolving model (SRM) simulations. The ICOsahedral Non-hydrostatic (ICON) modeling framework permits simulations ranging from numerical weather prediction to climate projections, making it an ideal target to develop neural network (NN) based parameterizations for sub-grid scale processes. Within the ICON framework, we train NN based cloud cover parameterizations with coarse-grained data based on realistic regional and global ICON SRM simulations. We set up three different types of NNs that differ in the degree of vertical locality they assume for diagnosing cloud cover from coarse-grained atmospheric state variables. The NNs accurately estimate sub-grid scale cloud cover from coarse-grained data that has similar geographical characteristics as their training data. Additionally, globally trained NNs can reproduce sub-grid scale cloud cover of the regional SRM simulation. Using the game-theory based interpretability library SHapley Additive exPlanations, we identify an overemphasis on specific humidity and cloud ice as the reason why our column-based NN cannot perfectly generalize from the global to the regional coarse-grained SRM data. The interpretability tool also helps visualize similarities and differences in feature importance between regionally and globally trained column-based NNs, and reveals a local relationship between their cloud cover predictions and the thermodynamic environment. Our results show the potential of deep learning to derive accurate yet interpretable cloud cover parameterizations from global SRMs, and suggest that neighborhood-based models may be a good compromise between accuracy and generalizability.
Machine learning based luminance analysis of a $μ$LED array
Authors: Steven Becker
Date: 2020-11-09 00:00:00
Tasks: BIG-bench Machine Learning

In the past years, the development of $\mu$LED arrays gained momentum since they combine the advantages of $\mu$LEDs, such as high brightness and longevity, with a high resolution of a micro-scaled structure. For the development, spatially resolved measurements of luminance and color of single $\mu$LEDs and the entire light-emitting surface are analyzed as they are crucial for the visual perception. However, the former is time intense in measurement and evaluation, and the latter suffers from interference caused by nonfunctional $\mu$LEDs. This paper presents a method to perform both analyzes with a single measurement using unsupervised machine learning. The results suggest that a precious reconstruction of the $\mu$LEDs and a more accurate characterization $\mu$LED arrays can be achieved.
Effect of Interdigital Capacitor on CRLH Leaky Wave Antenna Based on J-Shaped Metamaterial
Authors: Saeid Mohammadpour Jaghargh
Date: 2021-12-19 00:00:00

This paper aims to present a miniaturized novel reconfigurable composite right/left-handed leaky wave antenna (CRLH LWA) based on metamaterial as well as slow wave structure. In other words, the effect of interdigital capacitor (IDC) on LWA with periodic J-shaped metamaterial is investigated. This microstrip antenna is designed by cascading J-shaped metallic and IDC unit cells. Then it is simulated by full-wave ADS Momentum software. Advantageously, the proposed structure has tunable dispersion diagram that can be tuned by the number of IDC fingers. As will be clarified, altering the number of IDC fingers is able to adapt the equivalent capacitor of the antenna equivalent circuit. Thanks to the availability of RF switches technologies, changing the configuration of the IDCs can be practical. Furthermore, scanning the space from backward to forward region through broadside is one of the distinguishing features of the antenna. Indeed, the CRLH LWA continuously scans the space through broadside from -55 degree to +65 degree in {\phi} = 0 degree plane. The results of the investigation reveal that the LWA with two fingers, as more effective configuration, is a circularly polarized CRLH LWA and it is one of the highly desirable advantages of this structure. The axial ratio in the direction of the main beam for the backward region is lower than 3dB and it is improved in comparison to other regions. The deployment of IDC with variable figures in LWA with J-shaped metamaterial not only brings about the aforementioned significant features but also plays a pioneering role in this paper.
Drug repurposing for SARS-COV-2: A high-throughput molecular docking, molecular dynamics, machine learning, & ab-initio study
Authors: Jatin Kashyap, Dibakar Datta
Date: 2022-01-02 00:00:00
Tasks: Molecular Docking

A molecule of dimension 125nm has caused around 479 Million human infections (80M for the USA) & 6.1 Million human deaths (977,000 for the USA) worldwide and slashed the global economy by US$ 8.5 Trillion over two years. The only other events in recent history that caused comparative human life loss through direct usage (either by (wo)man or nature, respectively) of structure-property relations of 'nano-structures' (either (wo)man-made or nature, respectively) were nuclear bomb attacks of Japanese cities by the USA during World War II and 1918 Flu Pandemic. This molecule is SARS-CoV-2, which causes a disease known as COVID-19. The high liability cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for these & emerging diseases. As result, multiple vaccine candidates are discovered to avoid the infection in first place. But so far, there has been no success in finding fully effective therapeutics candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework. We have used the following robust framework to screen the ligands; Step-I: high throughput docking, Step-II: molecular dynamics, Step-III: density functional theory analysis. In total, we have analyzed 2.2 Million unique protein binding site/ligand combinations. The proteins were selected based on recent experimental studies. Step-I had filtered that number down to 10 ligands/protein based on molecular docking binding energy, further screening down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands/proteins were investigated in Step-II with a molecular dynamic based RMSD analysis. It finally suggested three ligands (ZINC1176619532, ZINC517580540, ZINC952855827) attacking different binding sites of the protein(7BV2), which were further analyzed in Step III.
Boosting the performance of anomalous diffusion classifiers with the proper choice of features
Authors: Patrycja Kowalek, Hanna Loch-Olszewska, Łukasz Łaszczuk, Jarosław Opała, Janusz Szwabiński
Date: 2021-12-30 00:00:00
Tasks: Classification, Task 2

Understanding and identifying different types of single molecules' diffusion that occur in a broad range of systems (including living matter) is extremely important, as it can provide information on the physical and chemical characteristics of particles' surroundings. In recent years, an ever-growing number of methods have been proposed to overcome some of the limitations of the mean-squared displacements approach to tracer diffusion. In March 2020, the Anomalous Diffusion (AnDi) Challenge was launched by a community of international scientists to provide a framework for an objective comparison of the available methods for anomalous diffusion. In this paper, we introduce a feature-based machine learning method developed in response to Task 2 of the challenge, i.e. the classification of different types of diffusion. We discuss two sets of attributes that may be used for the classification of single-particle tracking data. The first one was proposed as our contribution to the AnDi Challenge. The latter is the result of our attempt to improve the performance of the classifier after the deadline of the competition. Extreme gradient boosting was used as the classification model. Although the deep-learning approach constitutes the state-of-the-art technology for data classification in many domains, we deliberately decided to pick this traditional machine learning algorithm due to its superior interpretability. After the extension of the feature set our classifier achieved the accuracy of 0.83, which is comparable with the top methods based on neural networks.
Spatio-temporal dynamics of outbreak on a lattice with quenched mobility patterns
Authors: Jozef Černák
Date: 2021-12-21 00:00:00

We have designed a computational model of a virus spread near the outbreak threshold. Using computer simulation we studied the Susceptible - Infected - Recovered (SIR) process where in consequence of a force of habit that is manifested by the population mobility patterns, the recovered persons create the spatio-temporal patterns as the barriers to a virus transmission. The results show a spontaneous stopping of the virus spread without a need to infect the whole population, a non-trivial random noise of daily count of infected cases, and power laws of a cumulative count of infected cases. Outbreak evolution strongly depends on the initial conditions thus we concluded that the model has the features of chaotic systems that makes it difficult to predict its behaviors.
Relaxation to statistical equilibrium in stochastic Michaelis-Menten kinetics
Authors: Subham Pal, Manmath Panigrahy, R. Adhikari, Arti Dua
Date: 2021-12-20 00:00:00

The equilibration of enzyme and complex concentrations in deterministic Michaelis-Menten reaction networks underlies the hyperbolic dependence between the input (substrates) and output (products). This relationship was first obtained by Michaelis and Menten and then Briggs and Haldane in two asymptotic limits: `fast equilibrium' and `steady state'. In stochastic Michaelis-Menten networks, relevant to catalysis at single-molecule and mesoscopic concentrations, the classical analysis cannot be directly applied due to molecular discreteness and fluctuations. Instead, as we show here, such networks require a more subtle asymptotic analysis based on the decomposition of the network into reversible and irreversible sub-networks and the exact solution of the chemical master equation (CME). The reversible and irreversible sub-networks reach detailed balance and stationarity, respectively, through a relaxation phase that we characterise in detail through several new statistical measures. Since stochastic enzyme kinetics encompasses the single-molecule, mesoscopic and thermodynamic limits, our work provides a broader molecular viewpoint of the classical results, in much the same manner that statistical mechanics provides a broader understanding of thermodynamics.
A robust prediction from a minimal model of COVID-19 -- Can we avoid the third wave?
Authors: Sourav Chowdhury, Suparna Roychowdhury, Indranath Chaudhuri
Date: 2021-12-16 00:00:00

COVID-19 pandemic is one of the major disasters that humanity has ever faced. In this paper, we try to model the effect of vaccination in controlling the pandemic, particularly in context to the third wave which is predicted to hit globally. Here we have modified the SEIRD model by introducing a vaccination term. One of our main assumptions is that the infection rate (\b{eta}(t)) is oscillatory. This oscillatory nature has been discussed earlier in literature with reference to the seasonality of epidemics. However, in our case we invoke this nature of the infection rate (\b{eta}(t)) to model the cyclical behavior of the COVID-19 pandemic within a short period. This study focuses on a minimalistic approach where we have logically deduced that the infection rate (\b{eta}(t)) and the vaccination rate ({\lambda}) are the most important parameters while the other parameters can be assumed to be constants throughout the simulation. Finally, we have studied the rich interplay between the infection rate (\b{eta}(t)) and the vaccination rate ({\lambda}) on the infectious cases of COVID-19 and made some robust conclusions regarding the global behavior of this pandemic in near future.
RetroComposer: Composing Templates for Template-Based Retrosynthesis Prediction
Authors: Chaochao Yan, Peilin Zhao, Chan Lu, Yang Yu, Junzhou Huang
Date: 2021-12-20 00:00:00
Tasks: Prediction, Retrosynthesis, Single-step retrosynthesis

The main target of retrosynthesis is to recursively decompose desired molecules into available building blocks. Existing template-based retrosynthesis methods follow a template selection stereotype and suffer from limited training templates, which prevents them from discovering novel reactions. To overcome this limitation, we propose an innovative retrosynthesis prediction framework that can compose novel templates beyond training templates. As far as we know, this is the first method that uses machine learning to compose reaction templates for retrosynthesis prediction. Besides, we propose an effective reactant candidate scoring model that can capture atom-level transformations, which helps our method outperform previous methods on the USPTO-50K dataset. Experimental results show that our method can produce novel templates for 15 USPTO-50K test reactions that are not covered by training templates. We have released our source implementation.
A Grid-Structured Model of Tubular Reactors
Authors: Katsiaryna Haitsiukevich, Samuli Bergman, Cesar de Araujo Filho, Francesco Corona, Alexander Ilin
Date: 2021-12-13 00:00:00
Tasks: Chemical Process, model

We propose a grid-like computational model of tubular reactors. The architecture is inspired by the computations performed by solvers of partial differential equations which describe the dynamics of the chemical process inside a tubular reactor. The proposed model may be entirely based on the known form of the partial differential equations or it may contain generic machine learning components such as multi-layer perceptrons. We show that the proposed model can be trained using limited amounts of data to describe the state of a fixed-bed catalytic reactor. The trained model can reconstruct unmeasured states such as the catalyst activity using the measurements of inlet concentrations and temperatures along the reactor.
Classifying Turbulent Environments via Machine Learning
Authors: Michele Buzzicotti, Fabio Bonaccorso
Date: 2022-01-03 00:00:00
Tasks: Bayesian Inference, Earth Observation, Geophysics

The problem of classifying turbulent environments from partial observation is key for some theoretical and applied fields, from engineering to earth observation and astrophysics, e.g. to precondition searching of optimal control policies in different turbulent backgrounds, to predict the probability of rare events and/or to infer physical parameters labelling different turbulent set-ups. To achieve such goal one can use different tools depending on the system's knowledge and on the quality and quantity of the accessible data. In this context, we assume to work in a model-free setup completely blind to all dynamical laws, but with a large quantity of (good quality) data for training. As a prototype of complex flows with different attractors, and different multi-scale statistical properties we selected 10 turbulent 'ensembles' by changing the rotation frequency of the frame of reference of the 3d domain and we suppose to have access to a set of partial observations limited to the instantaneous kinetic energy distribution in a 2d plane, as it is often the case in geophysics and astrophysics. We compare results obtained by a Machine Learning (ML) approach consisting of a state-of-the-art Deep Convolutional Neural Network (DCNN) against Bayesian inference which exploits the information on velocity and enstrophy moments. First, we discuss the supremacy of the ML approach, presenting also results at changing the number of training data and of the hyper-parameters. Second, we present an ablation study on the input data aimed to perform a ranking on the importance of the flow features used by the DCNN, helping to identify the main physical contents used by the classifier. Finally, we discuss the main limitations of such data-driven methods and potential interesting applications.
Neural Born Iteration Method For Solving Inverse Scattering Problems: 2D Cases
Authors: Tao Shan, Zhichao Lin, Xiaoqian Song, Maokun Li, Fan Yang, Zhensheng Xu
Date: 2021-12-18 00:00:00

In this paper, we propose the neural Born iterative method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iterative method (TBIM). NeuralBIM employs independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions regarding the residuals. Two different schemes are presented in this paper, including the supervised and unsupervised learning schemes. With the data set generated by the method of moments (MoM), supervised NeuralBIM are trained with the knowledge of total fields and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded objective function founding on the governing equations of ISPs, which results in no requirement of total fields and contrasts for training. Numerical and experimental results further validate the efficacy of NeuralBIM.
Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning
Authors: Xiaoqian Chen, Balasubramanya T. Nadiga, Ilya Timofeyev
Date: 2021-12-16 00:00:00
Tasks: Transfer Learning

In this paper we demonstrate that reservoir computing can be used to learn the dynamics of the shallow-water equations. In particular, while most previous applications of reservoir computing have required training on a particular trajectory to further predict the evolution along that trajectory alone, we show the capability of reservoir computing to predict trajectories of the shallow-water equations with initial conditions not seen in the training process. However, in this setting, we find that the performance of the network deteriorates for initial conditions with ambient conditions (such as total water height and average velocity) that are different from those in the training dataset. To circumvent this deficiency, we introduce a transfer learning approach wherein a small additional training step with the relevant ambient conditions is used to improve the predictions.
Surrogate Model for Shallow Water Equations Solvers with Deep Learning
Authors: Yalan Song, Chaopeng Shen, Xiaofeng Liu
Date: 2021-12-20 00:00:00
Tasks: Deep Learning

Shallow water equations are the foundation of most models for flooding and river hydraulics analysis. These physics-based models are usually expensive and slow to run, thus not suitable for real-time prediction or parameter inversion. An attractive alternative is surrogate model. This work introduces an efficient, accurate, and flexible surrogate model, NN-p2p, based on deep learning and it can make point-to-point predictions on unstructured or irregular meshes. The new method was evaluated and compared against existing methods based on convolutional neural networks (CNNs), which can only make image-to-image predictions on structured or regular meshes. In NN-p2p, the input includes both spatial coordinates and boundary features that can describe the geometry of hydraulic structures, such as bridge piers. All surrogate models perform well in predicting flow around different types of piers in the training domain. However, only NN-p2p works well when spatial extrapolation is performed. The limitations of CNN-based methods are rooted in their raster-image nature which cannot capture boundary geometry and flow features exactly, which are of paramount importance to fluid dynamics. NN-p2p also has good performance in predicting flow around piers unseen by the neural network. The NN-p2p model also respects conservation laws more strictly. The application of the proposed surrogate model was demonstrated by calculating the drag coefficient $C_D$ for piers and a new linear relationship between $C_D$ and the logarithmic transformation of pier's length/width ratio was discovered.
Deep Surrogate for Direct Time Fluid Dynamics
Authors: Lucas Meyer, Louen Pottier, Alejandro Ribes, Bruno Raffin
Date: 2021-12-16 00:00:00
Tasks: Management, Specificity

The ubiquity of fluids in the physical world explains the need to accurately simulate their dynamics for many scientific and engineering applications. Traditionally, well established but resource intensive CFD solvers provide such simulations. The recent years have seen a surge of deep learning surrogate models substituting these solvers to alleviate the simulation process. Some approaches to build data-driven surrogates mimic the solver iterative process. They infer the next state of the fluid given its previous one. Others directly infer the state from time input. Approaches also differ in their management of the spatial information. Graph Neural Networks (GNN) can address the specificity of the irregular meshes commonly used in CFD simulations. In this article, we present our ongoing work to design a novel direct time GNN architecture for irregular meshes. It consists of a succession of graphs of increasing size connected by spline convolutions. We test our architecture on the Von K{\'a}rm{\'a}n's vortex street benchmark. It achieves small generalization errors while mitigating error accumulation along the trajectory.
A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction
Authors: Parisa Kavianpour, Mohammadreza Kavianpour, Ehsan Jahani, Amin Ramezani
Date: 2021-12-26 00:00:00
Tasks: Earthquake prediction, Prediction, Time Series Forecasting

Earthquakes, as natural phenomena, have continuously caused damage and loss of human life historically. Earthquake prediction is an essential aspect of any society's plans and can increase public preparedness and reduce damage to a great extent. Nevertheless, due to the stochastic character of earthquakes and the challenge of achieving an efficient and dependable model for earthquake prediction, efforts have been insufficient thus far, and new methods are required to solve this problem. Aware of these issues, this paper proposes a novel prediction method based on attention mechanism (AM), convolution neural network (CNN), and bi-directional long short-term memory (BiLSTM) models, which can predict the number and maximum magnitude of earthquakes in each area of mainland China-based on the earthquake catalog of the region. This model takes advantage of LSTM and CNN with an attention mechanism to better focus on effective earthquake characteristics and produce more accurate predictions. Firstly, the zero-order hold technique is applied as pre-processing on earthquake data, making the model's input data more proper. Secondly, to effectively use spatial information and reduce dimensions of input data, the CNN is used to capture the spatial dependencies between earthquake data. Thirdly, the Bi-LSTM layer is employed to capture the temporal dependencies. Fourthly, the AM layer is introduced to highlight its important features to achieve better prediction performance. The results show that the proposed method has better performance and generalize ability than other prediction methods.
The empire strikes back: Some responses to Bruineberg and colleagues
Authors: Maxwell J. D. Ramstead
Date: 2021-12-31 00:00:00

In their target paper, Bruineberg and colleagues provide us with a timely opportunity to discuss the formal constructs and philosophical implications of the free-energy principle. I critically discuss their proposed distinction between Pearl blankets and Friston blankets. I then critically assess the distinction between inference with a model and inference within a model in light of instrumentalist approaches to science.