Demo Data from EDBT 2026 / reviewers in the wild / expert
Todd C. Henry
dblp:351/2213
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 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 |
Computational fabrication · 50% Geometric modeling and processing · 50% | |
| Artificial intelligence
1 paper |
Generative modeling · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Geometric modeling and processing › topology optimization
structural topology optimization |
0.9 | 1 | 2025 | Generating Physically Sound Designs from Text and a Set of Physical Constraints · NeurIPS 2025 |
Machine learning › Generative modeling › diffusion model
text-to-image generation |
0.3 | 1 | 2025 | Generating Physically Sound Designs from Text and a Set of Physical Constraints · NeurIPS 2025 |
Methods — techniques the papers use, named apart from their topics
text-image model · 1.7differentiable physics simulation · 1.73-point bending test · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Generating Physically Sound Designs from Text and a Set of Physical ConstraintsabstractWe present TIDES, a text informed design approach for generating physically sound designs based on a textual description and a set of physical constraints. TIDES jointly optimizes structural (topology) and visual properties. A pre-trained text-image model is used to measure the design's visual alignment with a text prompt and a differentiable physics simulator is used to measure its physical performance. We evaluate TIDES on a series of structural optimization problems operating under different load and support conditions, at different resolutions, and experimentally in the lab by performing the 3-point bending test on 2D beam designs that are extruded and 3D printed. We find that it can jointly optimize the two objectives and return designs that satisfy engineering design requirements (compliance and density) while utilizing features specified by text. Gregory Barber, Todd C. Henry, Mulugeta A. Haile |
NeurIPS | 2 |