Nonlinear Dynamical Systems Modelling for Environmental Sustainability

Increasing global human population coupled with the climate change pose serious threats on the basic needs of society. The coming decades will witness the challenges associated with the food security, land availability, clean water availability, and energy security. These issues directly or indirectly affect the various components of the Earth’s Critical Zone (ECZ). Our natural resources are finite and hence a policy framework is urgently required to deal with the growing demand in food, clean energy, and water in sustainable ways. This work characterises some key components of the ECZ such as wetlands, through modelling and computational simulation approaches. A data-driven methodology known as the system identification is used to devise a nonlinear dynamic model of the tropical wetlands. The dataset used in the study corresponds to a Global Inundation Extent from Multi-Satellites. The model gives some useful insights about the dynamics of tropical wetlands and the possible effects of climate change on wetlands. The prediction power of this model is shown to be superior than the competing analytical models representing the inundation dynamics. This work also contributes towards the theoretical advancements in the nonlinear system identification method by proposing a new algorithm capable of performing the model structure selection in the NARMAX model class under the Approximate Bayesian Computation (ABC) framework. In addition to the data-driven approach, this thesis also switches to analytical modelling framework for investigating the sustainable ways of food production and climate change mitigation through a Negative Emission Technology (NET) known as enhanced weathering. The recent reports of Intergovernmental Panel on Climate Change have highlighted the need of an NET to meet the ambitious targets of lowering the global temperature. A process-based model representing the enhanced weathering of a mineral is developed and integrated with standard soil, vegetation process models. The integrated model is termed as the Integrated Enhanced Weathering Critical Zone Model, which is used to analyse the potentials of enhanced weathering in the UK conditions. The simulation results indicate that with the implementation of enhanced weathering in the UK farmlands, we can reduce the atmospheric carbon through sequestration as well as increase the crop yield substantially. In another words, food security and climate change mitigation can be addressed simultaneously. In a nutshell, the simulation results and analyses of this thesis can be used to design further experiments for investigating the ECZ processes like inundation dynamics and enhanced weathering. The results can also act as guidelines for framing the relevant policies towards environmental and food sustainability

Exploiting machine learning in multiscale modelling of materials

Recent developments in efficient machine learning algorithms have spurred significant interest in the materials community. The inherently complex and multiscale problems in Materials Science and Engineering pose a formidable challenge. The present scenario of machine learning research in Materials Science has a clear lacunae, where efficient algorithms are being developed as a separate endeavour, while such methods are being applied as ‘black-box’ models by others. The present article aims to discuss pertinent issues related to the development and application of machine learning algorithms for various aspects of multiscale materials modelling. The authors present an overview of machine learning of equivariant properties, machine learning-aided statistical mechanics, the incorporation of ab initio approaches in multiscale models of materials processing and application of machine learning in uncertainty quantification. In addition to the above, the applicability of Bayesian approach for multiscale modelling will be discussed. Critical issues related to the multiscale materials modelling are also discussed