I'm conlanger
Vu Ngoc Khiêm
As a conlanger, so-called continuum mechanics languager, I enhance the field of continuum mechanics by grounding it in the first principles of physics.
The craft of a conlanger
Having been developed over two centuries, the language of continuum mechanics is still not perfect and contains (knowledge) gaps in its grammar. Such grammatical gaps result in theories that are empirically applicable to only a subset of materials and fail in other scenarios. Thus, I enrich the field of continuum mechanics by grounding its theories in the first principles of physics. To this end, I lead a research group at the Department of Continuum Mechanics, RWTH Aachen University, Germany, focused on establishing (1) original continuum-mechanics domain-knowledge to enable the creation of interpretable machine-learning models, and (2) statistical-learning framework of large mechanical datasets. To date, my key contributions to the domain knowledge include: Analytical homogenization (Ah), Analytical Inelasticity (AI), and Thermodynamic Regularization (THERE).
About my name
In Vietnamese, my last name ‘Vu’ means the space, and my middle name ‘Ngoc’ means preciousness and rarity, likely reflecting my parents’ aspirations for me. I represent these in the website’s name as ‘vn’ by transforming the reference frame of ‘vu’, serving as a reminder of my origin. I prefer to be addressed as Khiêm (pronounced /’kim/) in both formal and informal settings, as this name begins with the German words ‘Kontinuum’/’Kautschuk’ and ends with ‘Mechanik’.
Long-term studies on natural rubber
Although I have studied various materials, my primary focus and interest have always been on natural rubber. It may not be a coincidence that my name begins with ‘Kautschuk’, the German word for natural rubber.
Natural rubber can be fascinating for a number of reasons. It exhibits exceptional resistance to crack growth due to the strain-induced crystallization phenomenon (SIC). The physical behavior of natural rubber is complex because it involves the full coupling of multiple fields, including deformation, temperature, and long-range interaction effects.
Nevertheless, I consider natural rubber a filter for theories in continuum mechanics (and its subfield, computational mechanics). A theory cannot be considered valid if it fails in specific cases (acting as a filter). Unfortunately, many recent theories in continuum and computational mechanics fall short when applied to natural rubber. The primary reason is that irreversibility in natural rubber does not necessarily imply strictly positive internal dissipation within the context of continuum mechanics. This might not be surprising to serious continuum mechanists, given that many concepts in the field were developed by applied mathematicians with limited physical understanding. However, these knowledge gaps present an opportunity for continuum physicists to step forward and address these challenges (JMPS2018, JMPS2022b, CMAME2024a).
In the age of data science, relying solely on data analysis through continuum and computational mechanics is insufficient for me. Therefore, I initiated my first steps in data collection by a collaboration with Prof. Jean-Benoît Le Cam (University of Rennes, France). Recently, we are the first who accurately discovered strain-induced crystallinity in cyclic loading of unfilled natural rubber using quantitative surface calorimetric data, thereby circumventing the complexity and cost of traditional X-ray diffraction measurements. This research facilitates microstructural observations of natural rubber in small laboratories and companies.
Ah
“AH” (analytical homogenization) is a statistical homogenization scheme, so-called mean field theory, that yields analytical microscopic strain measures for solving multiscale boundary value problems in diverse soft materials characterized by both solid-like and shell-like behavior and a wide range of mechanical phenomena (nonlinear elasticity, anisotropic damage, phase transition and fiber sliding). Such homogenization scheme offers physically based strain invariants that automatically guarantee interpretable and realistic behavior (such as non-affine deformation, material objectivity and material symmetry) in material models. The Ah method is particularly powerful in the age of data science, as it allows for the selection of material models without artificial restrictions, while maintaining the physical interpretability of results.
AI
“AI” (analytical inelasticity) is a nonlinear inelasticity theory describing the evolution of internal variables for any irreversible process in continuum mechanics. “AI” relies on a single thermodynamic potential, specifically the Helmholtz free energy, and achieves accuracy and performance efficiency in computing internal variables through analytical methods. This theory is applicable to a wide range of soft materials, including elastomers, hydrogels, and textile reinforcements. Notably, AI is effective for modeling natural rubbers and any strain-induced crystallizing polymers, materials for which the traditional double potential theory is inadequate.
there
“THERE” is a thermodynamically based regularization technique that addresses ill-posed problems in solid mechanics across a wide range of materials, while ensuring the interpretability and measurability of the regularization parameters. Its name reflects the adherence to the laws of thermodynamics and a single thermodynamic potential (i.e., the Helmholtz free energy). Notably, THERE is effective for deformation-induced phase transition in natural rubbers and crystallizing polymers, a phenomenon for which the traditional gradient damage and phase field methods are inadequate.
Research Projects
MUSE
Mechanics of polymers Using sensitive molecular force Sensors (funded by German Research Foundation, DFG, Project number 492017525).
I spearhead a research collaboration across three research institutes in Germany, integrating expertise from mechanists, chemists, and experimentalists. Our focus is on studying nonlocal effects (action at a distance) of damage in hydrogels, using mechanochemistry-induced fluorescence in combination with AI THERE.
ACTISONO
Sonopharmacology - Activation of drugs by ultrasound (funded by Leibniz Association, WGL, Project number W89/2023)
I collaborate within a highly interdisciplinary environment alongside chemists, biologists, medical doctors, and engineers to develop novel technologies for the activation of drug carriers using ultrasound. To this end, we conduct fluid-structure data analysis of a novel drug delivery system comprising polymer particles as drug carriers. The analysis outcomes are expected to inform synthesis guidance for optimizing drug release upon ultrasonication, that may unlock new therapeutic strategies for curing bacterial infections, liver damage, and neurological disorders.
DSLgene
Data-driven Statistical Learning of generalized mechanics of textile composites (funded by the Royal Society, Project number NIF\R1\241753)
This highly competitive Newton International Fellowship, with a global success rate of approximately 8%, marks my first attempt to extend my exceptional record of securing funding beyond Germany. In this project, I aim to revolutionize the current artificial intelligence modeling approaches of textile reinforcements by integrating machine learning with the extensive wealth of physics knowledge. This facilitates the development of robust material models of textile reinforcements with unparalleled reliability (in terms of extrapolatability), thereby enabling the prediction of unseen scenarios with confidence. This new capability will enable the virtual anticipation of forthcoming threats in areas such as armored vehicles, aircraft, and infrastructure, thereby enhancing preparedness and proactive defense strategies.