No abstract
Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times. We present a probabilistic, deep learning sea ice forecasting system, IceNet. The system has been trained on climate simulations and observational data to forecast the next 6 months of monthly-averaged sea ice concentration maps. We show that IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. This step-change in sea ice forecasting ability brings us closer to conservation tools that mitigate risks associated with rapid sea ice loss.
The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.
Buying a home is one of the most important buying decisions people have to make in their life. Although it is rare that the complete transaction happens online, real estate websites play an increasingly prominent role in the overall decision-making process. For example, the online real estate company Zillow hosts a database of more than 110 million homes in the U.S. and had 196 million unique users in 2019 [7]. Besides search and recommendation capabilities, today's real estate websites offer various decision aids to support buyers as well as professional and non-professional sellers in estimating the price of a property. According to Zillow, its home valuation model Zestimate R uses statistical and machine learning models to predict the market value of a property and the prediction "is within 10% of the final sale price more than 95% of the time" [6].From a theoretical perspective, real estate evaluation models are based on hedonic pricing models introduced by Lancaster and Rosen in the 1970s and 80s [9,16]. The main idea of hedonic pricing models is that the overall price of a good can be estimated by decomposing it into its constituting characteristics, determining the value contribution of each individual characteristic, and summing these contributions up to obtain the final price. In the past, most researchers implemented hedonic real estate pricing models through linear regression models [11,8]. While linear models have the advantage of being transparent and interpretable, they typically suffer from high bias, as their assumptions, such as linearity and additivity, often do not align with reality [4].
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