BEGIN:VCALENDAR VERSION:2.0 PRODID:-//wp-events-plugin.com//7.2.3.1//EN TZID:Asia/Kolkata X-WR-TIMEZONE:Asia/Kolkata BEGIN:VEVENT UID:194@cds.iisc.ac.in DTSTART;TZID=Asia/Kolkata:20260525T110000 DTEND;TZID=Asia/Kolkata:20260525T120000 DTSTAMP:20260507T142105Z URL:https://cds.iisc.ac.in/events/mtech-research-thesis-defense-hybrid-cds -25-may-2026-differential-algebraic-equation-dae-based-model-of-avalanche- dynamics-in-ag-hbn-memristor-and-reservoir-computing/ SUMMARY:Mtech Research Thesis Defense: HYBRID: CDS: 25\, May 2026 "Differen tial-Algebraic Equation (DAE) based model of Avalanche Dynamics in Ag-hBN memristor and Reservoir Computing. DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nMtech Research T hesis Defense\n\n\n\nSpeaker : Mr. JAYANTA PARI\nS.R. Number : 06-18-01-10 -12-22-2-22270\nTitle : Differential-Algebraic Equation(DAE) based model o f Avalanche Dynamics in Ag-hBN memristor and Reservoir Computing.\nThesis examiner : Prof. Rajendra Kr. Ray\, Mathematics\, IIT Mandi\nResearch Supe rvisor: Prof. Soumyendu Raha\nDate &\; Time : May 25\, 2026 (Monday) at 11:00 AM\nVenue : The Thesis Défense will be held on HYBRID Mode\n# 102 CDS Seminar Hall /MICROSOFT TEAMS\nPlease click on the following link to j oin the Thesis Defense:\nMS Teams link\n\n\n\nABSTRACT\n\nMemristive devic es based on percolative tunnelling networks exhibit complex collective dyn amics arising from the interaction of many nanoscale conductive paths. In particular\, Ag–hBN memristive systems have been experimentally shown to display avalanche-like current fluctuations and signatures of self-organi zed criticality. However\, the internal mechanisms responsible for these e mergent behaviours are difficult to access experimentally due to the highl y disordered and hidden nature of the network. This thesis develops a math ematical and computational framework to model and analyse avalanche dynami cs in such memristive networks and to explore their potential for reservoi r computing.\n\nThe device is modelled as a graph of nodes and edges\, whe re each edge represents a tunnelling or filamentary conduction channel who se conductance evolves in time. Kirchhoff’s circuit laws are enforced th rough an algebraic constraint\, while the internal filament dynamics are d escribed by nonlinear differential equations\, leading to a coupled differ ential–algebraic equation (DAE) system. Proper boundary conditions are i mposed to ensure a well-posed\, index-1 formulation suitable for stable nu merical integration. Joule heating–induced filament dissolution is incor porated through a discrete update rule.\n\nNumerical simulations are perfo rmed for both two-dimensional and quasi-three-dimensional network geometri es. The resulting conductance time series exhibits intermittent burst-like activity. Statistical analysis of these fluctuations reveals power-law di stributions of avalanche size and duration\, long-range temporal correlati ons\, and consistency with crackling-noise scaling relations.\n\nThe same DAE-based framework is then used as a physical reservoir for temporal info rmation processing. A scalar input signal is applied as a sequence of volt ages to multiple input nodes\, and the resulting currents at multiple outp ut nodes form a high-dimensional reservoir state. Linear readout training is performed using ridge regression to evaluate performance on benchmark t emporal tasks. The results demonstrate that the intrinsic dynamics of perc olative memristive networks can simultaneously support avalanche criticali ty and reservoir computing. All these highlight a direct connection betwee n material-level dynamics and computation in neuromorphic systems.\n\n\n\n ALL ARE WELCOME CATEGORIES:Events,Thesis Defense END:VEVENT BEGIN:VTIMEZONE TZID:Asia/Kolkata X-LIC-LOCATION:Asia/Kolkata BEGIN:STANDARD DTSTART:20250525T110000 TZOFFSETFROM:+0530 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE END:VCALENDAR