Prof Elena Surovyaktina

Potsdam Institute for Climate Impact Research (PIK)
Potsdam, Germany

Mobirise

7th Seminar: October 18, 2018

Monsoon forecast for central India: evidence from observations

Abstract
The variability of Indian monsoon implies two aspects wherein firstly, the monsoon season doesn't begin on any fixed date but is only determined by observations and secondly, monsoon begins on different dates in different parts of the country. These features thus make monsoon forecast extremely challenging in India. Additionally, even a two-week delay in monsoon can spell disaster on India's GDP, particularly in a situation where the country's 70% population is directly related to farming - which in turn is dependent on monsoons. Moreover, the forecasting of climate phenomena on a seasonal scale is even more challenging due to the absence of any recent historical precedent for changes in the climate system.

Thus, in order to overcome this challenge, a new methodology for monsoon forecasting is presented. This approach is based on a newly discovered feature of Indian summer monsoon wherein two geographic regions in the areas of the Eastern Ghats (EG) and North Pakistan (NP) act as tipping elements, which play a crucial role in the spatial organization of Monsoon. Observations of the near-surface air temperature and relative humidity in these areas allow forecasting of the monsoon onset and withdrawal dates for 40 and 70 days in advance respectively.

Moreover, the results show that the method used in this study allows predicting the monsoon retrospectively (over the period 1951-2015), as well as for the future. Also, successful predictions for 2016, 2017 and 2018 have validated the accuracy of the study and proved that such early predictions of monsoon timings are possible even under the conditions of climate change. Further, what is important to note is that the forecast of monsoon onset date through this study is the earliest, whereas the withdrawal date is the only one available in India.

Publications

Prof Sachin Gunthe

Department of Civil Engineering
IIT Madras, India

Mobirise

6th Seminar: September 10, 2018

Simplifying the brain: A vision for neuroscience in India

Abstract
The unprecedented industrial and urban growth is always associated with emission of various pollutants including aerosol particles (fine suspended particles in the atmosphere either solid or aqueous) and harmful gases. The atmospheric aerosol particles interact with the incoming solar radiation “directly” altering the Earth’s radiation budget (in a sense temperature) and are important and necessary elements in the process of cloud formation. The changes in lifetime, type, properties, and characteristics of cloud due to increasing aerosol concentration, in turn, “indirectly’ affects the Earth’s radiation budget. In addition, clouds are responsible for all type of precipitation on the Earth and therefore aerosol are important elements in governing the hydrological cycle on the Earth. Thus, the changing aerosol properties can perturb the cloud properties including lifetime and hydrological cycle of the planet. Based on the ice core data while we are accurately able to determine the pre-industrial era (year 1750) concentration of greenhouse gases, which are responsible for warming of the Earth surface we have no means to determine the historical cloud cover of the Earth or how it has changed, if at all, over last three centuries? Therefore, aerosol – cloud – climate interaction represents the largest uncertainty in current and future understanding of climate change and scientists are not able to give the precise estimate about how much temperature rise is expected at the end of this century. More to this, of late the research has also started focusing the role of aerosols of biological origin (bacteria, fungal spore, pollen grains, plant and animal fragments, etc.) on cloud formation and ecosystem health impact. On the other hand, some of the emitted harmful gases are precursors for the formation of surface ozone, which is harmful for human being and negatively impacts the agricultural production.

Indian climate system and ecosystem are very different and unique compared to rest of the world. While the former is characterized by the systematic and cyclic monsoon season and associated south westerly prevailing winds, the latter is marked by the presence of fragile and sensitive ecosystems like Indian Himalayan range and Western Ghats. Since last couple of decades, India has also experienced the tremendous industrial and urban growth and has been exerting regional and global climate impact. During my presentation, I will briefly touch the aspect related to studies we are carrying out about understanding the role of aerosols (including bioaerosols) and trace gases on Indian climate and ecosystem health. I will, in particular, elaborate about our current understanding about the role of changing aerosol properties over India in cloud and precipitation formation processes on regional to local scale. The challenges associated with ozone studies to assess the impact of ozone concentration over India for agricultural production loss will also be addressed.

Publications

Prof. Debasish Roy

Department of Civil Engineering
Indian Institute of Science Bangalore, India

Mobirise

5th Seminar: August 14, 2018

Complex dynamical systems: A tale of non-classical continuum models 


Abstract
The current scientific worldview of the physics of materials at extremely small or at extremely large spatial scales is fundamentally discrete. Even so, continuum field theoretic models, which offer a computationally less demanding setup, continue to be a major purveyor of understanding of the material behaviour. In this context, we recall that while continuum elasticity offers a rationally well-grounded field theory, such a theory for the highly inelastic response underlying the material’s journey to failure is yet to mature. Origins of inelastic motion may often be traced to the irreversible evolution of a certain class of material heterogeneities, herein called ‘defects’ in a generic sense, which disallows an Euclidean description for the evolving kinematic quantities associated with the material body. The non-Euclidean connection, which arises thereof and non-trivially modifies the notion of the ‘derivative’, thus alters the governing balance laws and, when inverted for the response, yield a potentially rich repertoire of non-classical phenomena in the solutions. The balance laws must however also be constitutively closed so as to be consistent with the non-equilibrium thermodynamic aspects of defect motion.

The question that arises is whether an advanced and complex model of this genre could provide, perhaps with a certain measure of experimental inputs, a faithful representation of the broad and diverse dynamical phenomena that precede brittle, quasi-brittle or ductile damage in material bodies. While a realization of such models – still a distant dream – would open up the possibility of a largely computer-driven optimal (i.e. defect-engineered) component design via the solution of an inverse problem, I will restrict my talk to our ongoing work pertaining to the development of a few such models for analysing plasticity-cum-damage and for a tractable computation of the dynamical response of certain metallic and non-metallic components of engineering interest.

Presentation slides


(Emeritus Profess0r, Retired)
Department of Physics
IIT Madras, India

Mobirise

4th Seminar: August 29, 2018

Recurrences in dynamical systems

Abstract
A general formalism is presented to describe recurrences and recurrence-time distributions in coarse-grained classical dynamical systems in discrete time. The connections between exit time, escape time and recurrence time distributions are deduced. The formalism is then applied to progressively more complex dynamical behaviour, starting with multiply periodic systems and progressing through quasi-periodic, chaotic and intermittently chaotic dynamics. The case of continuous-time dynamics is then considered. Finally, if time permits, some work on the analogues of recurrences in quantum dynamics will be described.

Presentation slides

Prof Karthik Raman

Department of Biotechnology
IIT Madras, India

Mobirise

3rd Seminar: April 24, 2018

Computational approaches to understanding complex biological networks

Abstract
Underlying every living cell is a complex interplay of molecular networks, that orchestrate cellular function. Systems biology seeks to understand cellular function, through building useful models of these complex networks. In our lab, we work on several areas of computational systems biology, and develop algorithms to understand and manipulate these biological networks. Through collaborations with computer scientists, chemical engineers and bioprocess engineers, we have built algorithms to predict pathways to synthesise different chemicals, developed strategies to re-engineer cellular metabolism to produce important metabolites like Vitamin E or hyaluronic acid, and predicted ways to design cellular regulatory networks. In this talk, I will give a brief overview of the work that goes on in our lab, and briefly overview how tools from network science, linear programming, control theory and machine learning have all been useful in developing our understanding of biological networks. I will also highlight some of the exciting challenges in the modelling of complex biological networks. We are an active member of the Robert Bosch Centre for Data Science and Artificial Intelligence (RBC DSAI) and the Initiative for Biological Systems Engineering (IBSE).


Presentation slides

Prof Arun Tangirala

Department of Chemical Engineering,
IIT Madras, India

Mobirise

2nd Seminar: April 5, 2018

Reconstruction of dynamic causal networks from data: Issues, remedies and challenges - Part 2

Abstract
In the first part of this series, an overview of causality, concepts and measures with focus on Granger Causality (GC) was provided. The second part shall begin with the specifics of GC-detection methodologies. Two applications to control systems are presented. In the central part of the talk, we broach on the effects of instantaneous causality, small sample sizes and mismatch in model and data generating process. Subsequently, remedies for the same are discussed. The talk closes with an extension of GC-detection methods to the non-linear case and a brief discussion on the effects of measurement errors and challenges ahead.

Presentation slides

Prof Mahesh Panchagnula

Department of Applied Mechanics 
IIT Madras, India

Mobirise

1st Seminar: March 15, 2018

Reconstruction of dynamic causal networks from data: Issues, remedies and challenges

Abstract
Complex network (graphical) representations of multivariate processes have assumed prominence in the recent era of multivariate time-series and process analysis. This imminent paradigm shift stems primarily from rapid developments in the fields of econometrics, social sciences and neuroscience towards reconstruction of causal (directed) networks . These combined developments have, in turn, propelled and nucleated the ideas of causality analysis in data-driven process engineering over the last decade. The first part of this talk presents an overview of the developments in causality analysis centred around the notion of Granger causality (GC). The second part of the talk presents results on reconstruction of dynamical GC networks from our research group. A systematic methodology for hierarchical reconstruction of Granger-causal graphs using spectral measures of GC is presented. In addition, two novel scalar correlation function measures for vector time-series modelling of multivariate processes towards efficient estimation of spectral GC measures are presented. The presentation is adorned with a few case studies illustrating applications of causality or directionality analysis. The presentation concludes with a brief discussion of few open-ended issues.

Presentation slides

Complex Systems & Dynamics     Indian Institute of Technology Madras     Chennai 600036     India