Masayuki Yano, University of Toronto

Research

numerical methods computational fluid dynamics partial differential equations reduced order modeling machine learning high performance computing

Latest funding

Predictive, efficient, and versatile digital twin algorithms for advanced manufacturing
NSERC · 2025 · Principal investigator
$214,284
Towards model reduction for efficient, reliable, and automated physics-based simulation of industry-scale problems
NSERC · 2024 · Principal investigator
$46,000
Robust Structural Topology Optimization
NSERC · 2019 · Co-investigator
$192,855

All 5 grants and awards

Predictive, efficient, and versatile digital twin algorithms for advanced manufacturing

Principal investigatorNSERC2025Advantage

Principal investigators: Yano, Masayuki

Keywords: Manufacturing processes and products; Mathematical sciences

$214,284

Towards model reduction for efficient, reliable, and automated physics-based simulation of industry-scale problems

Principal investigatorNSERC2024Discovery Grants Program - Individual

Principal investigators: Yano, Masayuki

Keywords: numerical methods; computational fluid dynamics; partial differential equations; reduced order modeling; machine learning; high performance computing

$46,000

Robust Structural Topology Optimization

Co-investigatorNSERC2019Collaborative Research and Development Grants

Principal investigators: Nair, Prasanth Balagopal

$192,855

Adaptive high-fidelity computational fluid dynamics

Principal investigatorNSERC2017Discovery Grants Program - Individual

Principal investigators: Yano, Masayuki

$210,000

Adaptive high-fidelity computational fluid dynamics

Principal investigatorNSERC2017Discovery Grants Program - Accelerator Supplements

Principal investigators: Yano, Masayuki

$120,000

From CIHR, NSERC and SSHRC funding decisions: CIHR since 2008, NSERC since 1991 and SSHRC since 1998, including their latest published competition results.

Write to this professor

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Applying for

Mention their NSERC grant from 2025

Subject: Prospective PhD student: numerical methods Dear Professor Yano, My name is [your name], and I am a [your current degree or role] at [your institution]. I am applying for a PhD position starting in [term and year]. Your research on numerical methods connects closely to my own work on [your research topic]. [One or two sentences on what you did and what you learned.] I also saw your NSERC-funded project "Predictive, efficient, and versatile digital twin algorithms for advanced manufacturing" (2025), and I would be glad to contribute to work in that direction. Are you accepting new PhD students for [term and year]? I have attached my CV and transcripts, and I would be glad to talk at a time that suits you. Thank you for your time. Kind regards, [your name]