Repository for Publications and Research Data
News from the ETH Library
Recently Added
Coupling phenomena in small networks of classical nonlinear parametric oscillators
Item type: Doctoral Thesis
Margiani, Gabriel (2026)
Parametric resonators have for a long time been the topic of intensive research aiming at a variety of applications and an understanding of nonlinear dynamics. While already a single resonator has applications (for example as an amplifier), and shows many interesting phenomena, coupling multiple of them opens up a vast field of challenging nonlinear effects, and has been suggested as a way towards alternative computing systems. Specifically, coupling many Kerr parametric oscillators (KPOs), each representing a two-level system or artificial spin, may be useful to find the ground state of an Ising Hamiltonian. This would provide a method to efficiently solve numerous NP-hard optimization problems which are relevant in everyday life (e.g. routing).
With returning focus on stochastic phenomena and Ising simulation, we experimentally study small networks of classical KPOs. Our resonator platforms comprise micro-electromechanical resonators with different geometries as well as inductively coupled electrical resonators. Featuring wide tunability, and multiple coupling strengths and quality factors, those devices allow us to select a flexible system with appropriate properties for each of our experiments. Furthermore, the individual measureability of all resonators in the network, as well as their agreement with an effective modeling as generic parametric resonator, enable us to reach detailed but generally applicable results.
Starting from a single resonator, we increase the network size up to a proof-of-concept measurement with four resonators. In the process, we explore below-threshold classical squeezing, phase state switching and the two-level-system equivalence in a single KPO. Moving to a resonator pair, we test Ising simulation with weak coupling, a method for limit-cycle generation, and external-force effects. After mapping the solution space of a three resonator network, we then turn to gate-based logic with three resonators and finally find the Ising ground state with three and four resonators.
It has been established that already two strongly coupled KPOs can have a solution space incompatible with the Ising model. We explain different driving regimes on three resonators, allowing or prohibiting successful Ising simulation. Thereby, we provide a basis for new research and possibly a successful implementation of an Ising simulator. Moreover, our results can form the starting point for various other classical and quantum experiments, theoretically linking resonator physics to other fields, expanding our understanding of nonlinear dynamics, and potentially providing advancements in technical applications like neuromorphic or in-memory computing.
Integrating Bayesian Groundwater Mixing Modeling With On-Site Helium Analysis to Identify Unknown Water Sources
Item type: Journal Article
Popp, Andrea; Scheidegger, Andreas; Moeck, Christian; et al. (2019)
Analyzing groundwater mixing ratios is crucial for many groundwater management tasks such as assessing sources of groundwater recharge and flow paths. However, estimating groundwater mixing ratios is affected by various uncertainties, which are related to analytical and measurement errors of tracers, the selection of end‐members, and finding the most suitable set of tracers. Although these uncertainties are well recognized, it is still not common practice to account for them. We address this issue by using a new set of tracers in combination with a Bayesian modeling approach, which explicitly considers the possibility of unknown end‐members while fully accounting for tracer uncertainties. We apply the Bayesian model we developed to a tracer set, which includes helium (⁴He) analyzed on site to determine mixing ratios in groundwater. Thereby, we identify an unknown end‐member that contributes up to 84 ± 9% to the water mixture observed at our study site. For the ⁴He analysis, we use a newly developed Gas Equilibrium Membrane Inlet Mass Spectrometer (GE‐MIMS), operated in the field. To test the reliability of on‐site ⁴He analysis, we compare results obtained with the GE‐MIMS to the conventional lab‐based method, which is comparatively expensive and labor intensive. Our work demonstrates that (i) tracer‐aided Bayesian mixing modeling can detect unknown water sources, thereby revealing valuable insights into the conceptual understanding of the groundwater system studied, and (ii) on‐site ⁴He analysis with the GE‐MIMS system is an accurate and reliable alternative to the lab‐based analysis.
Declining Runoff Sensitivity to Precipitation Following Permafrost Degradation: Insights From Event-Scale Runoff Response in the Yellow River Source Region
Item type: Journal Article
Tu, Zhuoyi; Wang, Taihua; Han, Juntai; et al. (2026)
Frozen ground, including permafrost and seasonally frozen ground (SFG), is a critical element of the cryosphere that strongly regulates hydrological processes in cold regions. It has been debated whether frozen ground degradation will make landscapes more, or less, sensitive to precipitation inputs; either outcome has profound implications for water resources and climate resilience. Using a data-driven approach based solely on observations, we quantify four decades of changes in event-scale runoff responses to daily precipitation in the source region of the Yellow River on the northeastern Tibetan Plateau. We apply Ensemble Rainfall-Runoff Analysis (ERRA), which infers hydrologic impulse responses directly from precipitation and streamflow time series without relying on model assumptions. This enables the assessment of nonlinear, nonstationary, and spatially heterogeneous hydrologic behavior across different frozen ground types and precipitation intensities. Results show that, relative to 1979–1998, the permafrost-dominant zone experienced a 47% reduction in peak runoff response per unit precipitation during 1999–2018 and a 32% decrease in the 25-day runoff coefficient, while the SFG-dominant region showed no substantial changes. The weakened runoff response in the permafrost-dominant zone, particularly under high-intensity precipitation (>10 mm d−1), likely reflects enhanced infiltration and subsurface storage driven by active-layer deepening and weakened near-surface seasonal freezing. These findings highlight the power of data-driven approaches in detecting hydrological regime shifts and provide critical insights for drought mitigation and flood risk assessment in permafrost-affected regions.
Multi-Objective Urban Observational Strategies: A Risk-Based Framework for Expanding Flood Sensor Networks
Item type: Journal Article
Brelsford, Christa; Coon, Ethan T.; Wang, Mark; et al. (2026)
In coupled human and natural systems, developing an observation strategy which maximizes insight into both the natural system and the human system is a challenging multi-objective optimization problem. In this article, we describe the expansion of a flood risk observation system in Southeast Texas designed to improve our understanding of both physical and socioeconomic exposure to hydrological hazards at fine spatial scales, in the context of a structured hazard-exposure-vulnerability risk framework. We describe a new approach for assessing the spatial extent through which a flood sensor's observations can be assumed to be relevant, and estimate the population served within each sensor's area of information using downscaled socio-demographic data. As hydrological observations and modeling move to ever finer scale, assessing the information they contain in the context of both social and natural systems becomes increasingly important for developing actionable scientific insights.
Streamflow Spatial Correlation Length Increases During Storms and Its Application in Data Assimilation Improves Streamflow Predictions
Item type: Journal Article
Timilsina, Sujana; Passalacqua, Paola (2026)
The spatial correlation of streamflow in a river network captures how flows at different locations relate to one another and influence the downstream response. In this study, we analyze the spatial correlation of streamflow and rainfall during storm events in three Texas watersheds: San Antonio (2015 and 2016 storms), Guadalupe (2018 storm), and Llano (2018 storm). We quantify spatial correlation using two semivariogram approaches: an isotropic semivariogram, which uses Euclidean distance, and a topological semivariogram, which uses hydrological distance that considers the nested catchment structure. We find that correlation lengths from topological semivariograms are longer than those from isotropic semivariograms, with storm-period lengths higher than those for non-storm periods. We incorporate the correlation length into a data assimilation approach that couples the output from the Conceptual Functional Equivalent (CFE) model with a state-space Muskingum method. Using the National Water Model (NWM) as a benchmark for performance comparison, we find that the base CFE model shows better timing accuracy than NWM outputs. Integration of spatial correlation into data assimilation shows modest gains at one site and larger improvements at another site. While the CFE model with data assimilation outperforms the NWM at two of the three validation sites, the NWM performs better at the remaining site due to its use of gridded precipitation data, unlike the spatially averaged values in CFE. These findings show that integrating the spatial correlation length in data assimilation improves streamflow predictions in high-flow scenarios, with performance strongly influenced by the quality and representation of precipitation inputs.