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Found 21 papers, 16 papers with code
Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
Deep learning-based surrogate models have demonstrated remarkable advantages over classical solvers in terms of speed, often achieving speedups of 10 to 1000 times over traditional partial differential equation (PDE) solvers.
Compositional Generative Inverse Design
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes that are more complex than those in the training data.
BENO: Boundary-embedded Neural Operators for Elliptic PDEs
Here we introduce Boundary-Embedded Neural Operators (BENO), a novel neural operator architecture that embeds the complex geometries and inhomogeneous boundary values into the solving of elliptic PDEs.
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation
We further investigate its adaptability to controlled data perturbations and examine the efficacy of in-context learning as a tool to enhance its adaptation.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
Learning Controllable Adaptive Simulation for Multi-resolution Physics
In this work, we introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model that jointly learns the evolution model and optimizes appropriate spatial resolutions that devote more compute to the highly dynamic regions.
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
Here, we introduce ViRel, a method for unsupervised discovery and learning of Visual Relations with graph-level analogy.
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way.
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
To model complex reservoir dynamics at both local and global scale, HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure.
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
We test our method in a 1D benchmark of nonlinear PDEs, 2D Navier-Stokes flows into turbulent phase and an inverse optimization of boundary conditions in 2D Navier-Stokes flow.
Graph Information Bottleneck
We design two sampling algorithms for structural regularization and instantiate the GIB principle with two new models: GIB-Cat and GIB-Bern, and demonstrate the benefits by evaluating the resilience to adversarial attacks.
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity.
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learning
Firstly, how can we make the learning models more flexible and efficient, so that agents can learn quickly with fewer examples?
Discovering Nonlinear Relations with Minimum Predictive Information Regularization
Identifying the underlying directional relations from observational time series with nonlinear interactions and complex relational structures is key to a wide range of applications, yet remains a hard problem.
Phase Transitions for the Information Bottleneck in Representation Learning
In the Information Bottleneck (IB), when tuning the relative strength between compression and prediction terms, how do the two terms behave, and what's their relationship with the dataset and the learned representation?
Pareto-optimal data compression for binary classification tasks
The goal of lossy data compression is to reduce the storage cost of a data set $X$ while retaining as much information as possible about something ($Y$) that you care about.
Learnability for the Information Bottleneck
However, in practice, not only is $\beta$ chosen empirically without theoretical guidance, there is also a lack of theoretical understanding between $\beta$, learnability, the intrinsic nature of the dataset and model capacity.
Neural Causal Discovery with Learnable Input Noise
Learning causal relations from observational time series with nonlinear interactions and complex causal structures is a key component of human intelligence, and has a wide range of applications.
Toward an AI Physicist for Unsupervised Learning
We investigate opportunities and challenges for improving unsupervised machine learning using four common strategies with a long history in physics: divide-and-conquer, Occam's razor, unification and lifelong learning.
Meta-learning autoencoders for few-shot prediction
Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0. 25% error, and 0. 46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.
