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Found 32 papers, 15 papers with code
Beyond Closure Models: Learning Chaotic-Systems via Physics-Informed Neural Operators
We propose an alternative end-to-end learning approach using a physics-informed neural operator (PINO) that overcomes this limitation by not using a closure model or a coarse-grid solver.
DBDH: A Dual-Branch Dual-Head Neural Network for Invisible Embedded Regions Localization
In addition, we introduce an extra segmentation head to segment the mask of the embedding region during training.
Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs
On complex downstream tasks with limited data, such as fluid flow simulations and fluid-structure interactions, we found CoDA-NO to outperform existing methods on the few-shot learning task by over $36\%$.
Plasma Surrogate Modelling using Fourier Neural Operators
Predicting plasma evolution within a Tokamak reactor is crucial to realizing the goal of sustainable fusion.
FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentation
One of the key designs of U-Net is the use of skip connections between the encoder and decoder, which helps to recover detailed information after upsampling.
Neural Operators for Accelerating Scientific Simulations and Design
Scientific discovery and engineering design are currently limited by the time and cost of physical experiments, selected mostly through trial-and-error and intuition that require deep domain expertise.
Nonlocality and Nonlinearity Implies Universality in Operator Learning
A popular variant of neural operators is the Fourier neural operator (FNO).
Forecasting subcritical cylinder wakes with Fourier Neural Operators
The learned FNO solution operator can be evaluated in milliseconds, potentially enabling faster-than-real-time modeling for predictive flow control in physical systems.
Fourier Continuation for Exact Derivative Computation in Physics-Informed Neural Operators
The physics-informed neural operator (PINO) is a machine learning architecture that has shown promising empirical results for learning partial differential equations.
Machine Learning Accelerated PDE Backstepping Observers
State estimation is important for a variety of tasks, from forecasting to substituting for unmeasured states in feedback controllers.
Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs
Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows.
Real-time high-resolution CO$_2$ geological storage prediction using nested Fourier neural operators
Carbon capture and storage (CCS) plays an essential role in global decarbonization.
Improving the Transferability of Adversarial Attacks on Face Recognition with Beneficial Perturbation Feature Augmentation
Though generating hard samples has shown its effectiveness in improving the generalization of models in training tasks, the effectiveness of utilizing this idea to improve the transferability of adversarial face examples remains unexplored.
An Adversarial Active Sampling-based Data Augmentation Framework for Manufacturable Chip Design
These synthetic mask images will augment the original limited training dataset used to finetune the lithography model for improved performance.
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
The resulting geo-FNO model has both the computation efficiency of FFT and the flexibility of handling arbitrary geometries.
Large Scale Mask Optimization Via Convolutional Fourier Neural Operator and Litho-Guided Self Training
Machine learning techniques have been extensively studied for mask optimization problems, aiming at better mask printability, shorter turnaround time, better mask manufacturability, and so on.
Generic Lithography Modeling with Dual-band Optics-Inspired Neural Networks
Lithography simulation is a critical step in VLSI design and optimization for manufacturability.
FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
FourCastNet accurately forecasts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor.
Adaptive Fourier Neural Operators: Efficient Token Mixers for Transformers
AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.
Physics-Informed Neural Operator for Learning Partial Differential Equations
Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution.
Efficient Token Mixing for Transformers via Adaptive Fourier Neural Operators
AFNO is based on a principled foundation of operator learning which allows us to frame token mixing as a continuous global convolution without any dependence on the input resolution.
U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow
Here we present U-FNO, a novel neural network architecture for solving multiphase flow problems with superior accuracy, speed, and data efficiency.
Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution
Recent studies have shown that deep neural networks are vulnerable to intentionally crafted adversarial examples, and various methods have been proposed to defend against adversarial word-substitution attacks for neural NLP models.
Neural Operator: Learning Maps Between Function Spaces
The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets.
Learning Dissipative Dynamics in Chaotic Systems
Chaotic systems are notoriously challenging to predict because of their sensitivity to perturbations and errors due to time stepping.
Unsupervised Word Segmentation with Bi-directional Neural Language Model
We present an unsupervised word segmentation model, in which the learning objective is to maximize the generation probability of a sentence given its all possible segmentation.
Fourier Neural Operator for Parametric Partial Differential Equations
The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces.
Unsupervised Summarization by Jointly Extracting Sentences and Keywords
We present RepRank, an unsupervised graph-based ranking model for extractive multi-document summarization in which the similarity between words, sentences, and word-to-sentence can be estimated by the distances between their vector representations in a unified vector space.
Multipole Graph Neural Operator for Parametric Partial Differential Equations
One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks.
Neural Operator: Graph Kernel Network for Partial Differential Equations
The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces.
Conditional Linear Regression
Work in machine learning and statistics commonly focuses on building models that capture the vast majority of data, possibly ignoring a segment of the population as outliers.
Learning Abduction under Partial Observability
The main shortcoming of this formulation of the task is that it assumes access to full-information (i. e., fully specified) examples; relatedly, it offers no role for declarative background knowledge, as such knowledge is rendered redundant in the abduction task by complete information.
