Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 62 tok/s
Gemini 2.5 Pro 48 tok/s Pro
GPT-5 Medium 14 tok/s Pro
GPT-5 High 13 tok/s Pro
GPT-4o 93 tok/s Pro
Kimi K2 213 tok/s Pro
GPT OSS 120B 458 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Quasiconvex relaxation of planar Biot-type energies and the role of determinant constraints (2501.10853v1)

Published 18 Jan 2025 in math.AP, math-ph, and math.MP

Abstract: We derive the quasiconvex relaxation of the Biot-type energy density $\lVert\sqrt{\operatorname{D}\varphiT \operatorname{D}\varphi}-I_2\rVert2$ for planar mappings $\varphi\colon\mathbb{R}2\to \mathbb{R}2$ in two different scenarios. First, we consider the case $\operatorname{D}\varphi\in\textrm{GL}+(2)$, in which the energy can be expressed as the squared Euclidean distance $\operatorname{dist}2(\operatorname{D}\varphi,\textrm{SO}(2))$ to the special orthogonal group $\textrm{SO}(2)$. We then allow for planar mappings with arbitrary $\operatorname{D}\varphi\in\mathbb{R}{2\times 2}$; in the context of solid mechanics, this lack of determinant constraints on the deformation gradient would allow for self-interpenetration of matter. We demonstrate that the two resulting relaxations do not coincide and compare the analytical findings to numerical results for different relaxation approaches, including a rank-one sequential lamination algorithm, trust-region FEM calculations of representative microstructures and physics-informed neural networks.

Summary

We haven't generated a summary for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Lightbulb On Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube