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DTSTAMP:20250522T212947Z
LOCATION:Mile High 4
DTSTART;TZID=America/Denver:20240729T104500
DTEND;TZID=America/Denver:20240729T121500
UID:siggraph_SIGGRAPH 2024_sess103@linklings.com
SUMMARY:Monte Carlo for PDEs
DESCRIPTION:Walkin’ Robin: Walk on Stars With Robin Boundary Conditions\n\
 nWe develop a grid-free Monte Carlo method for solving boundary value prob
 lems like the Poisson equation with Dirichlet, Neumann, and Robin boundary
  conditions. Unlike conventional PDE solvers, our method does not require 
 volumetric meshing or global solves. It is robust, embarrassingly parallel
 , sca...\n\n\nBailey Miller (Carnegie Mellon University), Rohan Sawhney (N
 VIDIA), and Keenan Crane and Ioannis Gkioulekas (Carnegie Mellon Universit
 y)\n---------------------\nNeural Monte Carlo Fluid Simulation\n\nWe prese
 nt a novel neural network representation for fluid simulation that augment
 s neural fields with explicitly enforced boundary conditions and a Monte C
 arlo pressure solver to eliminate all weakly enforced boundary conditions.
  Our method is mesh-free and can accurately represent vorticity phenom...\
 n\n\nPranav Jain (University of Southern California); Ziyin Qu (University
  of Pennsylvania); Peter Yichen Chen (Computer Science and Artificial Inte
 lligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT));
  and Oded Stein (University of Southern California)\n---------------------
 \nMonte Carlo for PDEs - Interactive Discussion\n\nAfter the summary prese
 ntations, attendees will participate in an interactive discussion. Distrib
 uted around the room will be a series of poster boards for authors to gath
 er around with the audience. Authors are invited to bring any material rel
 ated to their paper that could instigate further conver...\n\n------------
 ---------\nNeural Control Variates With Automatic Integration\n\nWe presen
 t a method that uses arbitrary neural network architectures as control var
 iates with automatic differentiation to improve Monte Carlo methods. Our a
 pproach creates unbiased, low-variance, and numerically stable Monte Carlo
  estimators for various problem setups. We demonstrate our method's a...\n
 \n\nZilu Li (Cornell University), Guandao Yang and Qingqing Zhao (Stanford
  University), Xi Deng (Cornell University), Leonidas Guibas (Stanford Univ
 ersity), Bharath Hariharan (Cornell University), and Gordon Wetzstein (Sta
 nford University)\n---------------------\nStochastic Computation of Baryce
 ntric Coordinates\n\nWe introduce an approach for computing barycentric co
 ordinates inside and outside a cage domain using only cage queries such as
  closest points and ray intersections. We show that this stochastic constr
 uction reproduces existing barycentric coordinates, e.g., harmonic and (po
 sitive) mean-value coordi...\n\n\nFernando de Goes (Pixar) and Mathieu Des
 brun (INRIA, Ecole Polytechnique)\n---------------------\nA Differential M
 onte Carlo Solver for the Poisson Equation\n\nWe introduce a general techn
 ique that differentiates solutions to the Poisson equation with Dirichlet 
 boundary conditions. Specifically, we devise a new boundary-integral formu
 lation for the derivatives with respect to arbitrary parameters including 
 shapes of the domain. Further, we develop an effic...\n\n\nZihan Yu (Unive
 rsity of California Irvine); Lifan Wu (NVIDIA); Zhiqian Zhou (University o
 f California Irvine); and Shuang Zhao (University of California Irvine, NV
 IDIA)\n---------------------\nVelocity-based Monte Carlo Fluids\n\nWe pres
 ent a velocity-based Monte Carlo fluid solver with operator splitting and 
 walk-on-boundary boundary handling, which overcomes the limitations of its
  existing vorticity-based counterpart. Our method can readily incorporate 
 various techniques drawn from conventional non-Monte Carlo methods, suc...
 \n\n\nRyusuke Sugimoto, Christopher Batty, and Toshiya Hachisuka (Universi
 ty of Waterloo)\n\nInterest Area: Research & Education\n\nKeyword: Animati
 on, Machine Learning, Rendering\n\nRegistration Category: Full Conference,
  Full Conference Supporter, Virtual Access, Exhibitor Full Conference, Mon
 day\n\nSession Chair: David Levin (University of Toronto, NVIDIA)
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