How Foundation Models Are Transforming Scientific Discovery

Foundation models such as large language models have made waves in language processing and computer vision, but their leap into scientific disciplines is both exciting and complex. Scientific fields demand not just accuracy, but strict adherence to physical laws and the ability to work with limited and proprietary data. Can these models rise to the challenge?

What Scientific Foundation Models Need

For foundation models to fuel breakthroughs in science, they must meet several essential requirements:

• Physical-constraint satisfaction ensures predictions never violate established scientific laws like conservation of mass or energy.
  
  
• Uncertainty quantification (UQ) provides probability-based forecasts, which is critical for applications where safety and risk matter.
  
  
• Data-efficient learning leverages synthetic data and domain expertise to overcome the scarcity of labeled data common in scientific work.

Innovations in Time Series Forecasting

Time series forecasting underpins everything from climate modeling to retail analytics. Traditional statistical models handle individual series, while newer deep learning models learn patterns across many. The Chronos foundation model advances this field by framing time series data as a sequence of tokens, much like words in a language model.

• Chronos can accurately forecast even chaotic systems by mimicking historical patterns, avoiding the pitfall of regressing to the mean.
  
  
• It addresses data scarcity by generating synthetic datasets and using advanced tokenization techniques like TSMix and wavelet transforms.
  
  
• Continuous values are embedded as discrete tokens, balancing detail with compatibility for language-model-style architectures.

Applications already span water, energy, and traffic forecasting, demonstrating that with thoughtful adaptation, foundation models can thrive in scientific settings.

Spatiotemporal Forecasting: The Next Step

Many scientific challenges, such as predicting weather or simulating fluid dynamics, require forecasting across both space and time. Traditionally, these tasks relied on numerical solvers for partial differential equations, but deep learning is gaining ground, especially with access to large datasets.

• Deep-learning weather prediction (DLWP) models, trained on rich datasets like ERA5, are now competitive with traditional numerical models.
  
  
• Different architectures offer trade-offs: SwinTransformer excels at short-term accuracy, while graph neural networks like GraphCast maintain stability over long-term, global forecasts.
  
  
• In aerodynamics, new high-fidelity datasets allow deep learning models to deliver fast, iterative solutions once considered too data-intensive to attempt.

Ensuring Physics and Quantifying Uncertainty

For foundation models to earn scientific trust, they must do more than fit the data—they must respect the rules of nature. Deep learning models can sometimes violate physical laws, but several strategies help mitigate this risk:

• Embedding physical constraints into the learning process ensures models obey conservation laws and other principles.
  
  
• Probabilistic frameworks like ProbConserv integrate these constraints into loss functions, boosting model accuracy and reliability even outside the training domain.
  
  
• Uncertainty quantification provides realistic confidence intervals, guiding further experiments and decision-making.
  
  
• Soft constraints in generative models, such as PreDiff for weather nowcasting, help outputs remain physically plausible.

Collaboration Unlocks the Potential

Foundation models will only gain widespread adoption in science if they reliably satisfy physics and provide actionable uncertainty estimates. Collaboration between machine learning researchers and domain experts is key to developing, validating, and deploying these advanced models in practice.

Conclusion

The intersection of foundation models and scientific discovery is a frontier with enormous promise. By blending data-driven techniques with scientific rigor, these models can accelerate innovation and improve forecasts—so long as we keep their predictions grounded in reality and governed by physical laws.

Source: Amazon Science Blog