Notes
dated notes and publications on what we're building and what we're learning along the way.
2026-07-14
Holistic Geometry and Topology Representation for Training Local Neural Models for Functional B-Rep Reconstruction
Theoretical foundation for training local neural models designed to understand, recognize, and reconstruct the geometric and functional characteristics of a component from a defined manufacturing domain
2026-08-14
Manufacturing-Constrained Primitive Fitting for Deterministic Mesh-to-B-Rep Reconstruction of Sheet-Metal Parts
What changes when the manufacturing process of the target part is known in advance. We restrict attention to sheet-metal bending, and we make the process constraints part of the representation rather than applying them as validation or repair after the fit.
2026-04-02
Data, Data, and More Data
Anyone building an AI model for a technical domain tends to talk about models. Which architecture, how many parameters, which fine-tuning method. That is the more interesting question, but rarely the decisive one. The decisive question is almost always the corpus.
Notes
dated notes and publications on what we're building and what we're learning along the way.
2026-07-14
Holistic Geometry and Topology Representation for Training Local Neural Models for Functional B-Rep Reconstruction
Theoretical foundation for training local neural models designed to understand, recognize, and reconstruct the geometric and functional characteristics of a component from a defined manufacturing domain
2026-08-14
Manufacturing-Constrained Primitive Fitting for Deterministic Mesh-to-B-Rep Reconstruction of Sheet-Metal Parts
What changes when the manufacturing process of the target part is known in advance. We restrict attention to sheet-metal bending, and we make the process constraints part of the representation rather than applying them as validation or repair after the fit.
2026-04-02
Data, Data, and More Data
Anyone building an AI model for a technical domain tends to talk about models. Which architecture, how many parameters, which fine-tuning method. That is the more interesting question, but rarely the decisive one. The decisive question is almost always the corpus.
Notes
dated notes and publications on what we're building and what we're learning along the way.
2026-07-14
Holistic Geometry and Topology Representation for Training Local Neural Models for Functional B-Rep Reconstruction
Theoretical foundation for training local neural models designed to understand, recognize, and reconstruct the geometric and functional characteristics of a component from a defined manufacturing domain
2026-08-14
Manufacturing-Constrained Primitive Fitting for Deterministic Mesh-to-B-Rep Reconstruction of Sheet-Metal Parts
What changes when the manufacturing process of the target part is known in advance. We restrict attention to sheet-metal bending, and we make the process constraints part of the representation rather than applying them as validation or repair after the fit.
2026-04-02
Data, Data, and More Data
Anyone building an AI model for a technical domain tends to talk about models. Which architecture, how many parameters, which fine-tuning method. That is the more interesting question, but rarely the decisive one. The decisive question is almost always the corpus.