ArticleScientific reports2025
Inverse design framework for 4D printed structures using the finite element method.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Review of Progress of AI in Biomimetics: From Biological Patterns to Closed-Loop Discovery.Biomimetics (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In this paper, we present an inverse design framework based on the finite element method (FEM) tailored for 4D printed structures capable of undergoing programmable, stimulus-responsive shape transformations over time. In contrast to conventional forward simulations, which predict deformation from a given initial configuration, our inverse approach seeks to determine the required printed geometry that will evolve into a desired final shape under specific external stimuli. The proposed framework incorporates viscoelasticity, geometric nonlinearity and time-dependent behavior into the FEM solver, formulating the inverse design task as an optimization problem. It enables users to specify custom target shapes and boundary conditions, thereby providing the flexibility needed to address a wide range of design scenarios. A comprehensive implementation workflow of the framework is also presented. To demonstrate the versatility and feasibility of our approach, we present two representative case studies: a classic bilayer actuator with 4D-printed normal hydrogel and a soft gripper with electrically responsive hydrogel. The results confirm the framework's accuracy and adaptability to different external stimulus, highlighting its potential as a practical computational design tool for the inverse design of 4D printed structures.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.