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:180@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20260128T110000
DTEND;TZID=Asia/Kolkata:20260128T120000
DTSTAMP:20260116T082007Z
URL:https://cds.iisc.ac.in/events/seminar-cds-102-january-28th-1100-neural
 -geometric-representations-and-immersed-approaches-for-scalable-pde-solver
 s-applications-in-agriculture-and-manufacturing/
SUMMARY:{Seminar} @ CDS: #102\, January 28th: 11:00: "Neural Geometric Repr
 esentations\, and Immersed Approaches for Scalable PDE Solvers: Applicatio
 ns in agriculture and manufacturing."
DESCRIPTION:Department of Computational and Data Sciences\nDepartment Semin
 ar\n\n\n\nSpeaker : Prof. Baskar Ganapathysubramanian\, Iowa State Univers
 ity\nTitle : Neural Geometric Representations\, and Immersed Approaches fo
 r Scalable PDE Solvers: Applications in agriculture and manufacturing\nDat
 e &amp\; Time: January 28th\, 2026 (Wednesday)\, 11:00 AM\nVenue : # 102\,
  CDS Seminar Hall\n\n\n\nABSTRACT\nHigh-fidelity simulation on complex geo
 metries remains constrained by meshing and geometry handling. I will discu
 ss our efforts in building scalable PDE solvers using adaptive\, incomplet
 e octrees\, with immersed boundary approaches\, and introducing a geometry
  pipeline based on implicit neural representations (INRs).\n\nWe explore b
 oth Immersogeometric Analysis (IMGA) and the Shifted Boundary Method (SBM)
  on adaptive incomplete octrees. We evaluate geometric flexibility\, accur
 acy and scalability alongside well-known challenges like cut-cell conditio
 ning\, high-order quadrature on slivers\, and load balancing at scale. The
  second part connects SBM to neural signed-distance fields: INRs provide c
 ontinuous geometry queries (distance\, normals) on demand\, eliminating ex
 plicit meshing and remeshing. Several benchmarks in fluid and solid mechan
 ics (e.g.\, cavity flows with obstacles\, porous/gyroidal structures\, and
  scanned shapes) illustrate accuracy and throughput comparable to mesh-bas
 ed pipelines.\n\nThis is collaborative work with A. Krishnamurthy\, M-C. H
 su (Iowa State)\, G. Scovazzi (Duke)\, and H. Sundar (Tufts)\, and is fund
 ed by NSF\, USDA-NIFA\, and DoD.\n\nBIO: Baskar Ganapathysubramaniam is is
  the Anderlik Professor of Engineering at Iowa State University. He receiv
 ed a B.Tech. from IIT Madras and a Ph.D. from Cornell University. He direc
 ts a curiosity-driven computational sustainability group focused on scient
 ific computing\, applied mathematics\, and machine learning with applicati
 ons to food\, energy\, and healthcare systems (me.iastate.edu/bglab). He i
 s Director of the NSF/USDA-funded AI Institute for Resilient Agriculture (
 aiira.iastate.edu) and Associate Director of the Translational AI Center a
 t Iowa State University.\n\nHost Faculty: Dr. Phani Motamarri\n\n\n\nALL A
 RE WELCOME
CATEGORIES:Events,Talks
END:VEVENT
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
X-LIC-LOCATION:Asia/Kolkata
BEGIN:STANDARD
DTSTART:20250128T110000
TZOFFSETFROM:+0530
TZOFFSETTO:+0530
TZNAME:IST
END:STANDARD
END:VTIMEZONE
END:VCALENDAR