Course Code

Course Tittle

Content

PH-601

Environment System Modelling

Fundamentals of Environmental System:

Overview of Environmental Systems and components (air, water, soil), Types of environmental pollution and techniques, Modeling principles: dynamic system, feedback and steady state behavior

Mathematical and Computational Modeling:

Difference equation and system dynamics, Model structure, formulation and validation, Use of modeling software like MATLAB Simulink, STELLA, VENSIM & etc.

Model Development and Classification:

Types of models (deterministic, stochastic, empirical, mechanistic), System diagrams and mathematical relationships, Model calibration, verification and validation

Air Pollution Modelling:

Modeling mobile source, air pollution invention, Role of mobile sources in urban air quality, Cohort models and Gaussian plume model

Surface and Groundwater Modeling:

Coupling models across environmental domain, GIS and remote sensing in model development, Uncertainty analysis and scenario development

Integrating Environmental Modeling:

Coupling models across environmental domain, GIS and remote sensing in model development, Uncertainty analysis and scenario

PH-602

Advance Environmental Physics

Radiative Physics and Spectroscopy:

Solar radiation and greenhouse effect, Black body radiation and Stefan-Boltzmann law, Light matter interaction, Einstein coefficients, UV light and bio-molecular damage,

Thermodynamics and Energy System:

First and Second Law of Thermodynamics, Heat engines and Carnot cycle, Energy transfer and storage, Pollution from Thermal system

Renewable Energy and Bio-Photonic System:

Solar, wind, hydro and bio-energy Physics, Physics of photosynthesis and energy conversion, Organic photocells and Grätzel cells (A dye sensitized solar cell), Bio-solar energy concepts

Nuclear and Radiation Physics:

Principles of nuclear fusion and fission, Radiation sources and their environmental impact, Health Hazard and radiation safety, nuclear fuel cycle and waste management

Environmental Fluid Dynamics and Pollutant Dispersion:

Mathematics of fluid flow: Navier-Stokes basics, Dispersion in rivers, lakes and aquifers, Gaussian plumes and turbulent jets, Monitoring pollution with remote sensing and optical tools

PH-603

Physics of Climate Changes

Climate System Component and Dynamics: Components: atmosphere, hydrosphere, biosphere, cryosphere, Climate feedback mechanisms and Interaction Pathways, Time scales of climate processes

Earth’s Radiation Balance:

Solar radiation, planetary albedo, Blackbody radiation and energy equilibrium, Stefan-Boltzmann law and radiative forcing

Climate Modeling and Simulation:

 General Circulation Models (GCMs) and Regional Climate Models (RCMs), Climate data sources, reanalysis, and assimilation, Scenario modeling: RCPs (Focus on climate outcomes), SSPs (Focus on socioeconomic pathways (population, GDP, policies)

Anthropogenic Influences and Forcings:

Greenhouse gas emissions and aerosols, Land use and urbanization effects, Climate Change attribution and detection

Climate Impact and Risk Analysis:

Climate sensitivity and uncertainty qualification, Sea level rise, glacier retreat, extreme weather patterns, Socioeconomic implications and resilience modeling

CT-647

Statistical Analysis

Statistical Inference & Hypothesis Testing: Confidence and significance level, Sample size determination, Point & interval estimates, Interval estimates and hypothesis for Population Mean, Population standard deviation, & Population proportion, Chi-square tests.

Regression and Correlation: Properties of least square, Simple linear regression, Nonlinear regression, Multiple regression, Statistical inference for regression, Choice of a Regression model, Correlation, Multiple and partial correlation, Coefficient of determination, Multicollinearity, Adequacy of the model.

Multivariate Statistics: Multivariate data and models, Multivariate Normal distribution, Principal Component Analysis, Factor Analysis, Canonical Correlation, Correspondence Analysis.

CT-652

Machine learning

Basic concepts of machine learning, supervised learning, logistic regression, perceptron, exponential family, generative learning algorithms, Gaussian discriminant analysis, Naive Bayes, Support vector machines, model selection and feature selection, Ensemble methods: Bagging, boosting,  Evaluating and debugging learning algorithms, bias variance tradeoffs, worst case learning, clustering, K-means, PCA, ICA, reinforcement learning.

CT-646

Research Methodologies

Introduction to Research: Nature, Scope and purpose, Developing the research question and establishing the framework, Research Process, Ethics and considerations

Types of Research: Types of Data and sources of data collection, Aims of research and its classification, Place & Time dimension in research

Research Methods: Scientific research designs, Experimental designs, Causal Comparative & Correlational Designs, Survey Research, Qualitative Research Designs, Historical Research

Methods and Tools: Instrumentation, Sampling, Validity & Reliabilit

CT -664

Quantum Computation and Information

• Basic concepts: Postulates of Quantum Mechanics
• Quantum bits – qubits, Dirac notation, combining qubits using the tensor product
• Measuring qubits, Performing operations on qubits
• The Bloch Sphere representation
• The quantum circuit model
• Simple quantum protocols: teleportation,
• superdense coding.
• Quantum Algorithms: Deutsch’s algorithm, Deutsch-Jozsa Algorithm and the Bernstein-Vazirani Algorithm
• Shor’s algorithm for factoring
• Grover’s algorithm for searching
• Entanglement and Bell’s theorem
• Open quantum systems
• Quantum error correction
• Quantum cryptography

• Emerging research topics from quantum computation and information