Complex Interconnected Systems:Risk-Informed Decisions for Situations of Compound Extremes

Speaker: Dr. Auroop Ganguly, Northeastern University

Date: September 16, 2025

Time: 1:00 PM ET

Location: Virtual (link forthcoming)

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Resources

• Destruction perfected
  Pinpointing the nodes whose removal most effectively disrupts a network has become a lot easier with the development of an efficient algorithm. Potential applications might include cybersecurity and disease control.
• Network science based quantification of resilience demonstrated on the Indian Railways Network
  The structure, interdependence, and fragility of systems ranging from power grids and transportation to ecology, climate, biology and even human communities and the Internet, have been examined through network science. While the response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science-based quantitative methods framework for measuring, comparing and interpreting hazard responses and as well as recovery strategies.
• Critical Infrastructures Resilience: Policy and Engineering Principles
  A first book on the analysis of interconnected infrastructure resilience
• Transdisciplinary electric power grid science
  Reliable electricity provides more than convenience; it fuels economies, governments, health care, education, and poverty reduction. As populations shift to cities and consume more energy, confronting the multifaceted challenges to reliable electricity becomes paramount.
• Recovery coupling in multilayer networks
  The increased complexity of infrastructure systems has resulted in critical interdependencies between multiple networks—communication systems require electricity, while the normal functioning of the power grid relies on communication systems. These interdependencies have inspired an extensive literature on coupled multilayer networks, assuming a hard interdependence, where a component failure in one network causes failures in the other network, resulting in a cascade of failures across multiple systems. While empirical evidence of such hard failures is limited, the repair and recovery of a network requires resources typically supplied by other networks, resulting in documented interdependencies induced by the recovery process. (the interconnection between transportation system and power grid has begun to be quantified in terms of resiliency).
• Catastrophic cascade of failures in interdependent networks
  Modern systems are coupled together and therefore should be modelled as interdependent networks. A fundamental property of interdependent networks is that failure of nodes in one network may lead to failure of dependent nodes in other networks.
• Beyond Markets and States
  Polycentric Governance of Complex Economic Systems: Contemporary research on the outcomes of diverse institutional arrangements for governing common-pool resources (CPRs) and public goods at multiple scales builds on classical economic theory while developing new theory to explain phenomena that do not fit in a dichotomous world of `the market' and `the state.'
• Usable climate science is adaptation science
  In the present historical moment, the only climate science that is truly usable is that which is oriented towards adaptation, because current policies and politics are so far from what would be needed to avert dangerous climate change that scientific uncertainty is not a limiting factor on mitigation.
• Physics-guided probabilistic modeling of extreme precipitation under climate change
  Earth System Models (ESMs) are the state of the art for projecting the effects of climate change. However, longstanding uncertainties in their ability to simulate regional and local precipitation extremes and related processes inhibit decision making. Existing state-of-the art approaches for uncertainty quantification use Bayesian methods to weight ESMs based on a balance of historical skills and future consensus. Here we propose an empirical Bayesian model that extends an existing skill and consensus based weighting framework and examine the hypothesis that nontrivial, physics-guided measures of ESM skill can help produce reliable probabilistic characterization of climate extremes.
• Climate Adaptation Informatics: Water Stress on Power Production
  Resilience to nonstationarity and deep uncertainty is a prerequisite to water security. Stakeholder planning horizons typically extend to about 30 years in water quantity or quality management, flood or drought hazard resilience, or the water-energy-food-ecosystems nexus. Projections of stressors, such as population, land use, stability assumptions of technologies, infrastructures, and organizations, are relatively more credible at the nearer term. However, compared to longer lead times of mid- to end-century and beyond, climate adaptation challenges are more acute.
• Coarse-graining
  The challenge of 'averaging out' is taken up by the complexity science community often with the language of `coarse-graining'
• DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution
  The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling.
• Hybrid physics-AI outperforms numerical weather prediction for extreme precipitation nowcasting
  Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists’ judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events.
• EPRI's Climate READi Initiative
  As 1-in-50 or 1-in-100-year extreme events of the past increase in frequency, and society increasingly depends on electricity, EPRI is strengthening the power sector’s collective approach to managing climate risk to the power system. And as the economy electrifies and decarbonizes, energy grid reliability and resilience will be paramount. Energy companies, regulators, policymakers, and other industry stakeholders require science-based insights about the future power system and the environment in which it will operate to identify optimal adaptation and resilience investments. EPRI’s collaborative model will convene the global thought leaders and scientific researchers necessary to build an informed and consistent approach.