BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20251028T140333Z
UID:10F91571-49D7-402A-AAB2-A4EC108D9A13
DTSTART;TZID=Asia/Kolkata:20251023T180000
DTEND;TZID=Asia/Kolkata:20251024T200000
DESCRIPTION:The IEEE NITK Piston Special Interest Group inaugurated its new
  series\, Piston Workshops\, aimed at providing students with exposure to 
 emerging technologies and software through structured\, hands-on learning 
 sessions. The initiative seeks to bridge theoretical understanding with pr
 actical implementation\, ensuring participants gain both conceptual clarit
 y and technical proficiency.\n\nThe first session under this series was co
 nducted on 23rd and 24th October 2025 by Mr. Nishant Patil\, Vice Chair of
  IEEE Piston. The topic of the workshop was Physics Informed Neural Networ
 ks (PINNs)\, a cutting-edge approach that integrates physical laws into ne
 ural network training for solving complex engineering problems.\n\nPartici
 pants were encouraged to bring their laptops and actively engage in coding
  exercises using Google Colab in Python.\n\n[]\n[]\n\nSpeaker(s): Nishant 
 Vinod Patil\n\nAgenda: \nDay 1 of the workshop focused on building foundat
 ional understanding. Attendees were introduced to the basic framework of N
 eural Networks (NNs) and Physics Informed Neural Networks (PINNs). The ses
 sion included demonstrations of key Python libraries such as NumPy\, Tenso
 rFlow\, and Matplotlib\, which were used to construct and visualize neural
  networks. Participants also learned to prepare customized loss functions 
 tailored to differential equations\, emphasizing the fusion of machine lea
 rning with physics-based modeling.\n\nDay 2 explored practical implementat
 ions of PINNs in solving engineering problems. Through guided examples\, s
 tudents appreciated the potential of PINNs in addressing classical problem
 s such as 2D heat conduction and the Poisson equation. The session conclud
 ed with discussions on the advantages\, limitations\, and future scope of 
 applying PINNs in computational engineering.\n\nRoom: CR 5\, Bldg: LHC C\,
  National Institute Of Technology Karnataka\, Surathkal\, MANGALURU\, Karn
 ataka\, India\, 575025
LOCATION:Room: CR 5\, Bldg: LHC C\, National Institute Of Technology Karnat
 aka\, Surathkal\, MANGALURU\, Karnataka\, India\, 575025
ORGANIZER:sb.nitk@ieee.org
SEQUENCE:18
SUMMARY:Piston Workshops - Introduction to Physics Informed Neural Networks
  (PINNs)
URL;VALUE=URI:https://events.vtools.ieee.org/m/510518
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;The IEEE NITK Piston Special Interest Grou
 p inaugurated its new series\,&amp;nbsp\;&lt;strong&gt;Piston Workshops&lt;/strong&gt;\, a
 imed at providing students with exposure to emerging technologies and soft
 ware through structured\, hands-on learning sessions. The initiative seeks
  to bridge theoretical understanding with practical implementation\, ensur
 ing participants gain both conceptual clarity and technical proficiency.&lt;/
 p&gt;\n&lt;p&gt;The first session under this series was conducted on 23rd and 24th 
 October 2025 by &lt;strong&gt;Mr. Nishant Patil&lt;/strong&gt;\, Vice Chair of IEEE Pi
 ston. The topic of the workshop was &lt;strong&gt;Physics Informed Neural Networ
 ks (PINNs)&lt;/strong&gt;\, a cutting-edge approach that integrates physical law
 s into neural network training for solving complex engineering problems.&lt;/
 p&gt;\n&lt;p&gt;Participants were encouraged to bring their laptops and actively en
 gage in coding exercises using Google Colab in Python.&lt;br&gt;&lt;br&gt;&lt;img src=&quot;ht
 tps://events.vtools.ieee.org/vtools_ui/media/display/75da6ec2-7b3e-41fb-85
 96-25fbac2dff74&quot; alt=&quot;&quot; width=&quot;650&quot; height=&quot;585&quot;&gt;&lt;br&gt;&lt;img src=&quot;https://eve
 nts.vtools.ieee.org/vtools_ui/media/display/2e58af81-21c7-4ab7-8bf5-314ee9
 cf9717&quot; alt=&quot;&quot; width=&quot;650&quot; height=&quot;439&quot;&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;b
 r /&gt;&lt;p&gt;&lt;strong&gt;Day 1&lt;/strong&gt; of the workshop focused on building foundati
 onal understanding. Attendees were introduced to the basic framework of Ne
 ural Networks (NNs) and Physics Informed Neural Networks (PINNs). The sess
 ion included demonstrations of key Python libraries such as &lt;strong&gt;NumPy\
 , TensorFlow\, and Matplotlib&lt;/strong&gt;\, which were used to construct and 
 visualize neural networks. Participants also learned to prepare customized
  loss functions tailored to differential equations\, emphasizing the fusio
 n of machine learning with physics-based modeling.&lt;/p&gt;\n&lt;p&gt;&lt;strong&gt;Day 2&lt;/
 strong&gt; explored practical implementations of PINNs in solving engineering
  problems. Through guided examples\, students appreciated the potential of
  PINNs in addressing classical problems such as &lt;strong&gt;2D heat conduction
 &lt;/strong&gt; and the &lt;strong&gt;Poisson equation&lt;/strong&gt;. The session concluded
  with discussions on the advantages\, limitations\, and future scope of ap
 plying PINNs in computational engineering.&lt;br&gt;&lt;br&gt;&lt;img src=&quot;https://events
 .vtools.ieee.org/vtools_ui/media/display/9a8aedba-8efd-4809-9d9c-f798bb155
 d97&quot; width=&quot;830&quot; height=&quot;259&quot;&gt;&lt;br&gt;&lt;img src=&quot;https://events.vtools.ieee.org
 /vtools_ui/media/display/01466a31-b1bd-4919-bdc2-c5494fa8fed3&quot; width=&quot;643&quot;
  height=&quot;557&quot;&gt;&lt;/p&gt;
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