[
https://issues.apache.org/jira/browse/AIRAVATA-3965?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Suresh Marru updated AIRAVATA-3965:
-----------------------------------
Labels: gsoc gsoc2025 mentor (was: )
> Facilitating computational experiment generation in AIRAVATA
> ------------------------------------------------------------
>
> Key: AIRAVATA-3965
> URL: https://issues.apache.org/jira/browse/AIRAVATA-3965
> Project: Airavata
> Issue Type: New Feature
> Reporter: Giri Krishnan
> Priority: Major
> Labels: gsoc, gsoc2025, mentor
>
> Computational sciences involve extensive experimentation which often involves
> searching over space of parameters, variables, functions and workflows.
> Individual researchers and groups often perform a large number of such
> searches to identify critical functional forms and workflows for any
> particular study. The goal of this work is to provide a tool that facilitates
> this search process. This will enable visualization, identifying or learning
> templates and generate potential experiments based on past experiments using
> LLM and neurosymbolic methods.
>
> This task requires the following specific goals for this work :
> # Provide visualization of past computational experiments: Tracking various
> computational experiments with various variations is often a challenging
> problem for individuals and groups of researchers. Often various adhoc
> approaches (directories, git etc) are used to track these changes, but often
> it is very difficult to provide an entire overview of past experiments. The
> goal of this work is to develop a visualization approach that allows to
> examine all the past experiments. This will require dimensionality reduction
> on the embeddings from LLMs which have been tested on its code generation
> abilities (eg. codellama, Llama 4 Maverick) for generating visualizations.
> Further comparison in the performance with standard code cloning and
> similarity measures will be required.
> # Identify template based on past experiment database: It is common for
> several computational experiments to share a common structure, in such cases
> identifying the 'template' allows for identifying common approaches in past
> experiments and to generate new ones. This work will need software engineer
> approach and AI based approaches to identify such templates. The templates
> will also be integrated with the visualization (in addition to embedding
> based visualization) allowing for examining the collections of experiments
> that belong to each template.
> # Generate new suggested experiments using templates and visualization
> guided search. Generation of new experiments is a key component of
> computational science work. To facilate this process, will require a visual
> interactive way to generate experiments based within the regions of previous
> experiments and also in the space where it was not previously explored. In
> addition, this will require generation of new experiments based on templates
> that were identified from the previous step. Template based generation could
> also provide a verifiable way to generate experiments.
>
--
This message was sent by Atlassian Jira
(v8.20.10#820010)
