document

Finite element-based framework to study the response of bituminous concrete pavements under different conditions

Kumar, Abhinav (author), Gupta, Ankit (author), Anupam, K. (author), Wagh, Vivek Pratap (author)

In most of developing countries across the world, pavement design is still based on an empirical approach that may result in premature failure or overdesigned pavements. A shift from an empirical to a semi-mechanistic or mechanistic approach is the need of modern time. In this regard, computational tools such as finite element (FE) are being...

journal article 2024

document

Uncertainties in wave-driven longshore sediment transport projections presented by a dynamic CMIP6-based ensemble

Zarifsanayei, Amin Reza (author), Antolínez, José A. Á. (author), Cartwright, Nick (author), Etemad-Shahidi, Amir (author), Strauss, Darrell (author), Lemos, Gil (author), Semedo, Alvaro (author), Kumar, Rajesh (author), Dobrynin, Mikhail (author), Akpınar, Adem (author)

In this study four experiments were conducted to investigate uncertainty in future longshore sediment transport (LST) projections due to: working with continuous time series of CSIRO CMIP6-driven waves (experiment #1) or sliced time series of waves from CSIRO-CMIP6-Ws and CSIRO-CMIP5-Ws (experiment #2); different wave-model-parametrization pairs...

journal article 2023

document

Neural cellular automata for solidification microstructure modelling

Tang, Jian (author), Kumar, Siddhant (author), De Lorenzis, Laura (author), Hosseini, Ehsan (author)

We propose Neural Cellular Automata (NCA) to simulate the microstructure development during the solidification process in metals. Based on convolutional neural networks, NCA can learn essential solidification features, such as preferred growth direction and competitive grain growth, and are up to six orders of magnitude faster than the...

journal article 2023

document

Modeling SAR Observables by Combining a Crop-Growth Model With Machine Learning

Nikaein, T. (author), Lopez Dekker, F.J. (author), Steele-Dunne, S.C. (author), Kumar, V. (author), Huber, M. (author)

In this article, our aim is to estimate synthetic aperture radar (SAR) observables, such as backscatter in VV and VH polarizations, as well as the VH/VV ratio, cross ratio, and interferometric coherence in VV, from agricultural fields. In this study, we use the decision support system for agrotechnology transfer (DSSAT) crop-growth simulation...

journal article 2023

document

Automated discovery of interpretable hyperelastic material models for human brain tissue with EUCLID

Flaschel, Moritz (author), Yu, Huitian (author), Reiter, Nina (author), Hinrichsen, Jan (author), Budday, Silvia (author), Steinmann, Paul (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We propose an automated computational algorithm for simultaneous model selection and parameter identification for the hyperelastic mechanical characterization of biological tissue and validate it on experimental data stemming from human brain tissue specimens. Following the motive of the recently proposed computational framework EUCLID ...

journal article 2023

document

Automated discovery of generalized standard material models with EUCLID

Flaschel, Moritz (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We extend the scope of our recently developed approach for unsupervised automated discovery of material laws (denoted as EUCLID) to the general case of a material belonging to an unknown class of constitutive behavior. To this end, we leverage the theory of generalized standard materials, which encompasses a plethora of important constitutive...

journal article 2023

document

A state-of-the-art review of measurement and modelling of skid resistance: The perspective of developing nation

Kumar, Abhinav (author), Tang, T. (author), Gupta, Ankit (author), Anupam, K. (author)

A critical review of lab and field measurement methodologies, harmonization in measuring techniques, and modelling of skid resistance of asphalt concrete pavement have been provided. Although several past studies have provided literature review on general topics of skid resistance, to the best of the author's knowledge, none of them have...

journal article 2023

document

Strain partitioning and damage initiation in a continuously cooled carbide free bainitic steel

Hajizad, O. (author), Kumar, A. (author), Petrov, R.H. (author), Sietsma, J. (author), Dollevoet, R.P.B.J. (author), Li, Z. (author)

Microscopic stress and strain partitioning control the mechanical and damage behavior of multiphase steels. Using a combined numerical and experimental approach, local strain distributions and deformation localization are characterized in a carbide free bainitic steel produced by continuous cooling. The microstructure of the steel consists of...

journal article 2022

document

Towards Understanding the Influence of Vertical Water Distribution on Radar Backscatter from Vegetation Using a Multi-Layer Water Cloud Model

Vermunt, P.C. (author), Steele-Dunne, S.C. (author), Khabbazan, S. (author), Kumar, V. (author), Judge, Jasmeet (author)

For a good interpretation of radar backscatter sensitivity to vegetation water dynamics, we need to know which parts of the vegetation layer control that backscatter. However, backscatter sensitivity to different depths in the canopy is poorly understood. This is partly caused by a lack of observational data to describe the vertical moisture...

journal article 2022

document

NN-EUCLID: Deep-learning hyperelasticity without stress data

Thakolkaran, P. (author), Joshi, A. (author), Zheng, Y. (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)

We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress–strain pairs, the approach only uses realistically measurable full-field displacement and global reaction force data, thus it lies...

journal article 2022

document

A processing chain for estimating crop biophysical parameters using temporal Sentinel-1 synthetic aperture radar data in cloud computing framework

Mandal, Dipankar (author), Kumar, V. (author), Lopez-Sanchez, Juan M. (author), Rao, Y. S. (author), McNairn, Heather (author), Bhattacharya, Avik (author), Mitchell, Scott (author)

Biophysical parameters are descriptors of crop growth and production estimates. Retrieval of these biophysical parameters from synthetic aperture radar sensors at operational scales is highly interesting given the increase in access to data from radar missions. Vegetation backscattering can be simulated using the water cloud model (WCM). Crop...

book chapter 2022

document

Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties

Joshi, A. (author), Thakolkaran, P. (author), Zheng, Y. (author), Escande, Maxime (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)

Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, we do not resort to stress data, but...

journal article 2022

document

Coevolution of machine learning and process-based modelling to revolutionize Earth and environmental sciences: A perspective

Razavi, Saman (author), Hannah, David M. (author), Elshorbagy, Amin (author), Kumar, Sujay (author), Marshall, Lucy (author), Solomatine, D.P. (author), Dezfuli, Amin (author), Sadegh, Mojtaba (author), Famiglietti, James (author)

Machine learning (ML) applications in Earth and environmental sciences (EES) have gained incredible momentum in recent years. However, these ML applications have largely evolved in ‘isolation’ from the mechanistic, process-based modelling (PBM) paradigms, which have historically been the cornerstone of scientific discovery and policy support....

journal article 2022

document

Building Rhythms: Reopening the workspace with indoor localisation

de Jong, Michiel (author), Triantafyllou, Giorgos (author), Spinoza Andreo, Guilherme (author), Dardavesis, Ioannis (author), Kumar, Pratyush (author), Maundri Prihanggo, Maundri (author)

Indoor localisation methods are an essential part for the management of COVID-19 restrictions, social distancing, and the flow of people in the indoor environment. Moving towards an open work space in this scenario requires effective real-time localisation services and tools, along with a comprehensive understanding of the 3D indoor space. This...

student report 2021

document

Building Rhythms: Reopening the Workspace with Indoor Localisation

Andreo, Guilherme Spinoza (author), Dardavesis, Ioannis (author), Jong, Michiel de (author), Kumar, Pratyush (author), Prihanggo, Maundri (author), Triantafyllou, Georgios (author), van der Vaart, C.G. (author), Verbree, E. (author)

Indoor localisation methods are an essential part for the management of COVID-19 restrictions, social distancing, and the flow of people in the indoor environment. Moving towards an open work space in this scenario requires effective real-time localisation services and tools, along with a comprehensive understanding of the 3D indoor space. This...

conference paper 2021

document

Unsupervised discovery of interpretable hyperelastic constitutive laws

Flaschel, Moritz (author), Kumar, Siddhant (author), De Lorenzis, Laura (author)

We propose a new approach for data-driven automated discovery of isotropic hyperelastic constitutive laws. The approach is unsupervised, i.e., it requires no stress data but only displacement and global force data, which are realistically available through mechanical testing and digital image correlation techniques; it delivers interpretable...

journal article 2021

document

A comparison between support vector machine and water cloud model for estimating crop leaf area index

Hosseini, Mehdi (author), McNairn, Heather (author), Mitchell, Scott (author), Robertson, Laura Dingle (author), Davidson, Andrew (author), Ahmadian, Nima (author), Bhattacharya, Avik (author), Borg, Erik (author), Kumar, V. (author)

The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures...

journal article 2021

document

Nature-based solutions efficiency evaluation against natural hazards: Modelling methods, advantages and limitations

Kumar, Prashant (author), Debele, Sisay E. (author), Sahani, Jeetendra (author), Rawat, Nidhi (author), Marti-Cardona, Belen (author), Alfieri, S.M. (author), Basu, Bidroha (author), Basu, Arunima Sarkar (author), Menenti, M. (author)

Nature-based solutions (NBS) for hydro-meteorological risks (HMRs) reduction and management are becoming increasingly popular, but challenges such as the lack of well-recognised standard methodologies to evaluate their performance and upscale their implementation remain. We systematically evaluate the current state-of-the art on the models...

review 2021

document

Optimising the performance of complex sociotechnical systems in high-stress, high-speed environments: The formula 1 pit stop test case

Slater, David (author), Hill, Rees (author), Kumar, Maneesh (author), Ale, B.J.M. (author)

In analysing the performance of complex sociotechnical systems, of particular interest is the inevitable and inherent variability that these systems exhibit, but can normally tolerate, in successfully operating in the real world. Knowing how that variability propagates and impacts the total function mix then allows an understanding of emergent...

journal article 2021

document

Modelling and managing massive 3D data of the built environment

Kumar, Kavisha (author)

A 3D city model is a digital representation of the spatial features in an urban<br/>environment. Buildings, terrain, vegetation, water bodies, etc. all form an integral part of a 3D city model. The possibility to enrich these city models with additional application-specific information, whether new semantics or geometry, further increases their...

doctoral thesis 2020