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Mitsuki Hamamichi

dblp:364/4025 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Rendering · 50% Computer animation and physical simulation · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Rendering › appearance acquisition › material acquisition
material parameter estimation
0.712023
Non-Newtonian ViRheometry via Similarity Analysis · ACM Trans. Graph. 2023
Computer animation and physical simulation › fluid simulation
non-newtonian fluid simulation
0.712023
Non-Newtonian ViRheometry via Similarity Analysis · ACM Trans. Graph. 2023

Methods — techniques the papers use, named apart from their topics

similarity analysis · 0.7hessian analysis · 0.7gradient-based optimization · 0.7
YearPublicationVenuePosition
2023 Non-Newtonian ViRheometry via Similarity Analysis
abstract
We estimate the three Herschel-Bulkley parameters (yield stress σ Y , power-law index n , and consistency parameter η ) for shear-dependent fluid-like materials possibly with large-scale inclusions, for which rheometers may fail to provide a useful measurement. We perform experiments using the unknown material for dam-break (or column collapse) setups and capture video footage. We then use simulations to optimize for the material parameters. For better match up with the simple shear flow encountered in a rheometer, we modify the flow rule for the elasto-viscoplastic Herschel-Bulkley model. Analyzing the loss landscape for optimization, we realize a similarity relation ; material parameters far away within this relation would result in matched simulations, making it hard to distinguish the parameters. We found that by exploiting the setup dependency of the similarity relation, we can improve on the estimation using multiple setups, which we propose by analyzing the Hessian of the similarity relation. We validate the efficacy of our method by comparing the estimations to the measurements from a rheometer (for materials without large-scale inclusions) and show applications to materials with large-scale inclusions, including various salad or pasta sauces, and congee.
Mitsuki Hamamichi, Kentaro Nagasawa, Masato Okada, Ryohei Seto, Yonghao Yue
ACM Trans. Graph.1