Physics-Informed Uncertainty Enables Reliable AI-driven Design

Inverse design is a central goal in much of science and engineering, including frequency-selective surfaces (FSS) that are critical to microelectronics for telecommunications and optical metamaterials. Traditional surrogate-assisted optimization methods using deep learning can accelerate the design process but do not usually incorporate uncertainty quantification, leading to poorer optimization performance due to erroneous predictions in data-sparse regions. Here, we introduce and validate a fundamentally different paradigm of Physics-Informed Uncertainty, where the degree to which a model's prediction violates fundamental physical laws serves as a computationally-cheap and effective proxy for predictive uncertainty. By integrating physics-informed uncertainty into a multi-fidelity uncertainty-aware optimization workflow to design complex frequency-selective surfaces within the 20 - 30 GHz range, we increase the success rate of finding performant solutions from less than 10% to over 50%, while simultaneously reducing computational cost by an order of magnitude compared to the sole use of a high-fidelity solver. These results highlight the necessity of incorporating uncertainty quantification in machine-learning-driven inverse design for high-dimensional problems, and establish physics-informed uncertainty as a viable alternative to quantifying uncertainty in surrogate models for physical systems, thereby setting the stage for autonomous scientific discovery systems that can efficiently and robustly explore and evaluate candidate designs.

Incremental Multistep Forecasting of Battery Degradation Using Pseudo Targets

Data-driven models accurately perform early battery prognosis to prevent equipment failure and further safety hazards. Most existing machine learning (ML) models work in offline mode which must consider their retraining post-deployment every time new data distribution is encountered. Hence, there is a need for an online ML approach where the model can adapt to varying distributions. However, existing online incremental multistep forecasts are a great challenge as there is no way to correct the model of its forecasts at the current instance. Also, these methods need to wait for a considerable amount of time to acquire enough streaming data before retraining. In this study, we propose iFSNet (incremental Fast and Slow learning Network) which is a modified version of FSNet for a single-pass mode (sample-by-sample) to achieve multistep forecasting using pseudo targets. It uses a simple linear regressor of the input sequence to extrapolate pseudo future samples (pseudo targets) and calculate the loss from the rest of the forecast and keep updating the model. The model benefits from the associative memory and adaptive structure mechanisms of FSNet, at the same time the model incrementally improves by using pseudo targets. The proposed model achieved 0.00197 RMSE and 0.00154 MAE on datasets with smooth degradation trajectories while it achieved 0.01588 RMSE and 0.01234 MAE on datasets having irregular degradation trajectories with capacity regeneration spikes.

Compound Fault Diagnosis for Train Transmission Systems Using Deep Learning with Fourier-enhanced Representation

Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems. Data-driven fault diagnosis models have several advantages over traditional methods in terms of dealing with non-linearity, adaptability, scalability, and automation. However, existing data-driven models are trained on separate transmission components and only consider single faults due to the limitations of existing datasets. These models will perform worse in scenarios where components operate with each other at the same time, affecting each component's vibration signals. To address some of these challenges, we propose a frequency domain representation and a 1-dimensional convolutional neural network for compound fault diagnosis and applied it on the PHM Beijing 2024 dataset, which includes 21 sensor channels, 17 single faults, and 42 compound faults from 4 interacting components, that is, motor, gearbox, left axle box, and right axle box. Our proposed model achieved 97.67% and 93.93% accuracies on the test set with 17 single faults and on the test set with 42 compound faults, respectively.

Memristive Nanowire Network for Energy Efficient Audio Classification: Pre-Processing-Free Reservoir Computing with Reduced Latency

Speech recognition is a key challenge in natural language processing, requiring low latency, efficient computation, and strong generalization for real-time applications. While software-based artificial neural networks (ANNs) excel at this task, they are computationally intensive and depend heavily on data pre-processing. Neuromorphic computing, with its low-latency and energy-efficient advantages, holds promise for audio classification. Memristive nanowire networks, combined with pre-processing techniques like Mel-Frequency Cepstrum Coefficient extraction, have been widely used for associative learning, but such pre-processing can be power-intensive, undermining latency benefits. This study pioneers the use of memristive and spatio-temporal properties of nanowire networks for audio signal classification without pre-processing. A nanowire network simulation is paired with three linear classifiers for 10-class MNIST audio classification and binary speaker generalization tests. The hybrid system achieves significant benefits: excellent data compression with only 3% of nanowire output utilized, a 10-fold reduction in computational latency, and up to 28.5% improved classification accuracy (using a logistic regression classifier). Precision and recall improve by 10% and 17% for multispeaker datasets, and by 24% and 17% for individual speaker datasets, compared to raw data classifiers. This work provides a foundational proof of concept for utilizing memristive nanowire networks (NWN) in edge-computing devices, showcasing their potential for efficient, real-time audio signal processing with reduced computational overhead and power consumption, and enabling the development of advanced neuromorphic computing solutions.