AutoMATES: Automated Model Assembly from Text, Equations, and Software
- URL: http://arxiv.org/abs/2001.07295v1
- Date: Tue, 21 Jan 2020 00:33:40 GMT
- Title: AutoMATES: Automated Model Assembly from Text, Equations, and Software
- Authors: Adarsh Pyarelal and Marco A. Valenzuela-Escarcega and Rebecca Sharp
and Paul D. Hein, Jon Stephens, Pratik Bhandari, HeuiChan Lim, Saumya Debray,
Clayton T. Morrison
- Abstract summary: AutoMATES aims to build semantically-rich unified representations of models from scientific code and publications.
Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations.
- Score: 5.364472782227326
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Models of complicated systems can be represented in different ways - in
scientific papers, they are represented using natural language text as well as
equations. But to be of real use, they must also be implemented as software,
thus making code a third form of representing models. We introduce the
AutoMATES project, which aims to build semantically-rich unified
representations of models from scientific code and publications to facilitate
the integration of computational models from different domains and allow for
modeling large, complicated systems that span multiple domains and levels of
abstraction.
Related papers
- M, Toolchain and Language for Reusable Model Compilation [1.3048920509133806]
M is a toolchain and modeling language designed to support system modeling and multi-target compilation.<n>It provides constructs for modeling system entities, message-based interactions, and time- or state-triggered reactions.
arXiv Detail & Related papers (2025-11-19T09:21:46Z) - Show-o2: Improved Native Unified Multimodal Models [57.34173415412808]
Show-o2 is a native unified multimodal models that leverage autoregressive modeling and flow matching.<n>Built upon a 3D causal variational autoencoder space, unified visual representations are constructed through a dual-path of spatial (-temporal) fusion.
arXiv Detail & Related papers (2025-06-18T15:39:15Z) - Textual-Based vs. Thinging Machines Conceptual Modeling [0.0]
Software engineers typically interpret the domain description in natural language and translate it into a conceptual model.<n>Three approaches are used in this domain modeling: textual languages, diagrammatic languages, and a mixed based of text and diagrams.
arXiv Detail & Related papers (2025-06-03T09:00:26Z) - A Model Is Not Built By A Single Prompt: LLM-Based Domain Modeling With Question Decomposition [4.123601037699469]
In real-world domain modeling, engineers usually decompose complex tasks into easily solvable sub-tasks.
We propose an LLM-based domain modeling approach via question decomposition, similar to developer's modeling process.
Preliminary results show that our approach outperforms the single-prompt-based prompt.
arXiv Detail & Related papers (2024-10-13T14:28:04Z) - Consistent Autoformalization for Constructing Mathematical Libraries [4.559523879294721]
Autoformalization is the task of automatically translating mathematical content written in natural language to a formal language expression.
This paper proposes the coordinated use of three mechanisms, most-similar retrieval augmented generation (MS-RAG), denoising steps, and auto-correction with syntax error feedback (Auto-SEF) to improve autoformalization quality.
arXiv Detail & Related papers (2024-10-05T15:13:22Z) - Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities [89.40778301238642]
Model merging is an efficient empowerment technique in the machine learning community.
There is a significant gap in the literature regarding a systematic and thorough review of these techniques.
arXiv Detail & Related papers (2024-08-14T16:58:48Z) - Learnable & Interpretable Model Combination in Dynamic Systems Modeling [0.0]
We discuss which types of models are usually combined and propose a model interface that is capable of expressing a variety of mixed equation based models.
We propose a new wildcard topology, that is capable of describing the generic connection between two combined models in an easy to interpret fashion.
The contributions of this paper are highlighted at a proof of concept: Different connection topologies between two models are learned, interpreted and compared.
arXiv Detail & Related papers (2024-06-12T11:17:11Z) - A Graphical Modeling Language for Artificial Intelligence Applications
in Automation Systems [69.50862982117127]
An interdisciplinary graphical modeling language that enables the modeling of an AI application as an overall system comprehensible to all disciplines does not yet exist.
This paper presents a graphical modeling language that enables consistent and understandable modeling of AI applications in automation systems at system level.
arXiv Detail & Related papers (2023-06-20T12:06:41Z) - PaLM-E: An Embodied Multimodal Language Model [101.29116156731762]
We propose embodied language models to incorporate real-world continuous sensor modalities into language models.
We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks.
Our largest model, PaLM-E-562B with 562B parameters, is a visual-language generalist with state-of-the-art performance on OK-VQA.
arXiv Detail & Related papers (2023-03-06T18:58:06Z) - Language Model Cascades [72.18809575261498]
Repeated interactions at test-time with a single model, or the composition of multiple models together, further expands capabilities.
Cases with control flow and dynamic structure require techniques from probabilistic programming.
We formalize several existing techniques from this perspective, including scratchpads / chain of thought, verifiers, STaR, selection-inference, and tool use.
arXiv Detail & Related papers (2022-07-21T07:35:18Z) - Facilitating automated conversion of scientific knowledge into
scientific simulation models with the Machine Assisted Generation,
Calibration, and Comparison (MAGCC) Framework [0.0]
The Machine Assisted Generation, Comparison, and Computational (MAGCC) framework provides machine assistance and automation of recurrent crucial steps and processes.
MAGCC bridges systems for knowledge extraction via natural language processing or extracted from existing mathematical models.
The MAGCC framework can be customized any scientific domain, and future work will integrate newly developed code-generating AI systems.
arXiv Detail & Related papers (2022-04-21T19:30:50Z) - Automated Creation and Human-assisted Curation of Computable Scientific
Models from Code and Text [2.3746609573239756]
Domain experts cannot gain a complete understanding of the implementation of a scientific model if they are not familiar with the code.
We develop a system for the automated creation and human-assisted curation of scientific models.
We present experimental results obtained using a dataset of code and associated text derived from NASA's Hypersonic Aerodynamics website.
arXiv Detail & Related papers (2022-01-28T17:31:38Z) - Model-agnostic multi-objective approach for the evolutionary discovery
of mathematical models [55.41644538483948]
In modern data science, it is more interesting to understand the properties of the model, which parts could be replaced to obtain better results.
We use multi-objective evolutionary optimization for composite data-driven model learning to obtain the algorithm's desired properties.
arXiv Detail & Related papers (2021-07-07T11:17:09Z) - S2RMs: Spatially Structured Recurrent Modules [105.0377129434636]
We take a step towards exploiting dynamic structure that are capable of simultaneously exploiting both modular andtemporal structures.
We find our models to be robust to the number of available views and better capable of generalization to novel tasks without additional training.
arXiv Detail & Related papers (2020-07-13T17:44:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.