Date: November 5 to 7, 2026
Location: Westin, Arlington
Address: 801 North Glebe Road,
Arlington, Virginia 22203Â
Room: TBD
Scientific discovery is increasingly constrained not by the lack of simulation tools, but by the immense computational, data, and energy costs required to explore complex scientific systems at scale. Modern AI methods have demonstrated extraordinary capabilities in large-data regimes, yet many frontier scientific domains—such as climate modeling, fluid dynamics, materials discovery, fusion, subsurface systems, and biological modeling—operate in precisely the opposite setting: sparse observations, expensive simulations, limited experimental access, and strict reliability requirements.
The primary goal of the symposium is to unite researchers working on efficient, physically grounded, and resource-aware AI methods for scientific discovery. The symposium will highlight emerging advances in areas such as data-efficient learning, hybrid neuro-mechanistic modeling, scientific foundation models, uncertainty-aware AI, operator learning, and compute-aware reasoning for scientific applications. Ultimately, the symposium seeks to catalyze a new research community around parsimonious scientific machine learning (Parsimonious SciML) and establish a roadmap for scalable scientific AI that prioritizes efficiency, trustworthiness, and scientific utility rather than brute-force computation alone. This bridge builds upon the success of three previous symposia organized on a related topic of Knowledge-Guided Machine Learning at the AAAI bridge program in 2024, 2025 and 2026. Accepted papers will appear in AAAI proceedings.
Day 1: November 5
10.30am - 11.00am
12.30pm - 2.00pm
2.00pm - 2.30pm
RP: Extrusion Networks to Overcome the Curse of (Simulation) Dimensionality
Authors: Harsha Vardhan Dasari, Arya Vaidya, Shital Adhikari and Nikhil Muralidhar.
RP: Sparse-Context Science using Conditional Neural Processes.
Authors: Emily Shinkle, Prescott Alexander, Nicholas Lubbers and Yulia Pimonova.
RP: Beyond One Pixel: Parameter-Efficient Spatial Context Adaptation for Hyperspectral Image Classification.
Authors: Siqi Du, Deepak Kandel and Dimah Dera.
2.30pm - 3.30pm
RP: Extrusion Networks to Overcome the Curse of (Simulation) Dimensionality.
Exploring Joint-Embedding Predictive Architectures for Self-Supervised Representation Learning of Powder X-ray Diffraction Patterns.
On challenges in scientific knowledge representation and active learning in a self-driving additive manufacturing testbed.
RP: Auditing Process Knowledge for Parsimonious and Explainable Industrial Cyberattack Detection.
RP: Sparse-Context Science using Conditional Neural Processes.
RP: Beyond One Pixel: Parameter-Efficient Spatial Context Adaptation for Hyperspectral Image Classification.
RP: AeroGNN: A Multi-Fidelity Physics-Informed Graph Neural Network for Aerodynamic Design Optimization of Tall Buildings.
A Trustworthy Multimodal Learning Framework for Predicting Lipid Nanoparticle–RNA Delivery Performance.
RP: Conditioning GMFlow with a Gated Cross-Correlation Prior for Particle Image Velocimetry.
A Constrained Semi-Supervised Learning Algorithm for Deforestation Mapping.
3.30pm - 4.00pm
4.00pm - 5.00pm
Day 2: November 6
10.30am - 11.00am
11.00am - 11.30am
Geotechnical Parameter Identification beyond Force Reconstruction
Author: Honglai Peng, Rodrigo Borela, Kimberlie Staheli and J. David Frost.
RP: Multi-Fidelity Surrogates for Gappy, Expensive Simulation Data in Gravitational-Wave Astronomy
Author: Tousif Islam.
EC: Provable Extrapolation for Neural PDE Surrogates.
Author: Ignacy Nieweglowski.Â
11.30am - 12.30pm
Charging the Prompt to the Budget: LLM-Proposed Descriptors Reduce Selection Risk, Not Error.
RP:SAGE: Self-Evolving Acquisition through Guided Escalation for Subtle Anomaly Segmentation.
Geotechnical Parameter Identification beyond Force Reconstruction.
RP: DLens: Closed-Loop AI Agents for Parsimonious Scientific Machine Learning in Dark Matter Gravitational Lensing.
RP: Multi-Fidelity Surrogates for Gappy, Expensive Simulation Data in Gravitational-Wave Astronomy.
Characterizing LLM Code Generation for Computational Simulations.
Classifier-pruned Bayesian optimization for particle accelerator tuning: Exploring temporally structured manifold of 6D beam phase space.
RP: IDENTIFY: Conditional Finite-Sample Law-Set Certificates and Ambiguity-Directed Experiments.
RP:Neural ODEs with Invariants by Construction.
DF: Fidelity and Efficiency in Control-Theoretic On-the-Fly Learning for Molecular Dynamics.
12.30pm - 2.00pm
2.00pm - 2.30pm
DF: Fidelity and Efficiency in Control-Theoretic On-the-Fly Learning for Molecular Dynamics.
Author: Michael Tynes.
Charging the Prompt to the Budget: LLM-Proposed Descriptors Reduce Selection Risk, Not Error.
Author: Varshitha Manjunath and Srimonti Dutta.
RP: DLens: Closed-Loop AI Agents for Parsimonious Scientific Machine Learning in Dark Matter Gravitational Lensing.
Authors: Aatmaj Amol Salunke, Mywish Anand, Pranath Reddy Kumbam, Michael W. Toomey and Sergei V. Gleyzer.Â
3.30pm - 4.00pm
4.00pm - 5.00pm
Day 3: November 7
10.30am - 11.00am
12.30 - 12.40pm
Organizing Committee
Steering Committee