# Deep Neural Networks to Recover Unknown Physical Parameters from Oscillating Time Series

arXiv: https://arxiv.org/abs/2101.03850 · HTML: https://mlsanity.com/paper/2101.03850/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 11 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling](https://mlsanity.com/paper/1710.11431/) (2017)
- [Which Neural Net Architectures Give Rise To Exploding and Vanishing Gradients?](https://mlsanity.com/paper/1801.03744/) (2018)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Auto-Encoding Total Correlation Explanation](https://mlsanity.com/paper/1802.05822/) (2018)
- [Building DNN Acoustic Models for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1406.7806/) (2014)
- [Unsupervised Learning via Total Correlation Explanation](https://mlsanity.com/paper/1706.08984/) (2017)

## Built on by (most cited first, 4 of 4)

- [Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems](https://mlsanity.com/paper/2305.02251/) (2023)
- [Forecasting the outcome of spintronic experiments with Neural Ordinary Differential Equations](https://mlsanity.com/paper/2108.02318/) (2021)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [Aligning AI-driven discovery with human intuition](https://mlsanity.com/paper/2410.07397/) (2024)

## Abstract (arXiv)

Deep neural networks (DNNs) are widely used in pattern-recognition tasks for which a human comprehensible, quantitative description of the data-generating process, e.g., in the form of equations, cannot be achieved. While doing so, DNNs often produce an abstract (entangled and non-interpretable) representation of the data-generating process. This is one of the reasons why DNNs are not extensively used in physics-signal processing: physicists generally require their analyses to yield quantitative information about the studied systems. In this article we use DNNs to disentangle components of oscillating time series, and recover meaningful information. We show that, because DNNs can find useful abstract feature representations, they can be used when prior knowledge about the signal-generating process exists, but is not complete, as it is particularly the case in "new-physics" searches. To this aim, we train our DNN on synthetic oscillating time series to perform two tasks: a regression of the signal latent parameters and signal denoising by an Autoencoder-like architecture. We show that the regression and denoising performance is similar to those of least-square curve fittings (LS-fit) with true latent parameters' initial guesses, in spite of the DNN needing no initial guesses at all. We then explore applications in which we believe our architecture could prove useful for time-series processing in physics, when prior knowledge is incomplete. As an example, we employ DNNs as a tool to inform LS-fits when initial guesses are unknown. We show that the regression can be performed on some latent parameters, while ignoring the existence of others. Because the Autoencoder needs no prior information about the physical model, the remaining unknown latent parameters can still be captured, thus making use of partial prior knowledge, while leaving space for data exploration and discoveries.