Merge branch 'lululxvi:master' into fix-variable-compile

.github/workflows/build.yml

@@ -23,9 +23,9 @@ jobs:
         os: [ubuntu-latest, macos-latest, windows-latest]
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
-      - uses: actions/setup-python@v4
+      - uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python-version }}
 

.github/workflows/release.yml

@@ -10,15 +10,15 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
       - name: Build SDist and wheel
         run: pipx run build
 
       - name: Upload SDist and wheel
-        uses: actions/upload-artifact@v3
+        uses: actions/upload-artifact@v4
         with:
           path: dist/*
           if-no-files-found: error
@@ -33,12 +33,12 @@ jobs:
 
     steps:
       - name: Download SDist and wheel
-        uses: actions/download-artifact@v3
+        uses: actions/download-artifact@v4.1.7
         with:
           name: artifact
           path: dist
 
-      - name: Publish on PyPI
-        uses: pypa/gh-action-pypi-publish@v1.6.4
+      - name: Publish package to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
         with:
           password: ${{ secrets.PYPI_API_TOKEN }}

README.md

@@ -140,9 +140,9 @@ First off, thanks for taking the time to contribute!
 
 ## The Team
 
-DeepXDE was developed by [Lu Lu](https://github.com/lululxvi) under the supervision of Prof. [George Karniadakis](https://www.brown.edu/research/projects/crunch/george-karniadakis) at [Brown University](https://www.brown.edu) from the summer of 2018 to 2020, supported by [PhILMs](https://www.pnnl.gov/computing/philms). DeepXDE was originally self-hosted in Subversion at Brown University, under the name SciCoNet (Scientific Computing Neural Networks). On Feb 7, 2019, SciCoNet was moved from Subversion to GitHub, renamed to DeepXDE.
+DeepXDE was developed by [Lu Lu](https://github.com/lululxvi) under the supervision of Prof. [George Karniadakis](https://www.brown.edu/research/projects/crunch/george-karniadakis) at [Brown University](https://www.brown.edu) from the summer of 2018 to 2020. DeepXDE was originally self-hosted in Subversion at Brown University, under the name SciCoNet (Scientific Computing Neural Networks). On Feb 7, 2019, SciCoNet was moved from Subversion to GitHub, renamed to DeepXDE.
 
-DeepXDE is currently maintained by [Lu Lu](https://github.com/lululxvi) at [Yale University](https://www.yale.edu) with major contributions coming from many talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: [Paul Escapil-Inchauspé](https://github.com/pescap), [Jialin Li](https://github.com/lijialin03), [Saransh Chopra](https://github.com/Saransh-cpp), [Zongren Zou](https://github.com/ZongrenZou), [Sensen He](https://github.com/HydrogenSulfate), [Zhongyi Jiang](https://github.com/Jerry-Jzy), [Anran Jiao](https://github.com/anranjiao), [Shunyuan Mao](https://github.com/smao-astro).
+DeepXDE is currently maintained by [Lu Lu](https://github.com/lululxvi) at [Yale University](https://www.yale.edu) with major contributions coming from many talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: [Paul Escapil-Inchauspé](https://github.com/pescap), [Zongren Zou](https://github.com/ZongrenZou), [Jialin Li](https://github.com/lijialin03), [Saransh Chopra](https://github.com/Saransh-cpp), [Sensen He](https://github.com/HydrogenSulfate), [Vladimir Dudenkov](https://github.com/vl-dud), [Anran Jiao](https://github.com/anranjiao), [Zhongyi Jiang](https://github.com/Jerry-Jzy), [Shunyuan Mao](https://github.com/smao-astro).
 
 ## License
 

deepxde/backend/backend.py

@@ -502,3 +502,42 @@ def sparse_dense_matmul(x, y):
     Returns:
         Tensor: The multiplication result.
     """
+
+
+###############################################################################
+# Regularization
+
+
+def l1_regularization(l1):
+    """A regularizer that applies a L1 regularization penalty or L1 weight decay.
+
+    Warning:
+        The implementation may vary across different backends.
+
+    Args:
+        l1 (float): L1 regularization factor. 
+    """
+
+
+def l2_regularization(l2):
+    """A regularizer that applies a L2 regularization penalty or L2 weight decay.
+
+    Warning:
+        The implementation may vary across different backends.
+
+    Args:
+        l2 (float): L2 regularization factor.
+    """
+
+
+def l1_l2_regularization(l1, l2):
+    """A regularizer that applies both L1 and L2 regularization penalties or 
+    L1 and L2 weight decay.
+
+    Warning:
+        The implementation may vary across different backends.
+
+    Args:
+        l1 (float): L1 regularization factor.
+        l2 (float): L2 regularization factor.
+    """

deepxde/backend/paddle/tensor.py

@@ -229,3 +229,11 @@ def matmul(x, y):
 
 def sparse_dense_matmul(x, y):
     return paddle.sparse.matmul(x, y)
+
+
+def l1_regularization(l1):
+    return paddle.regularizer.L1Decay(coeff=l1)
+
+
+def l2_regularization(l2):
+    return paddle.regularizer.L2Decay(coeff=l2)

deepxde/backend/tensorflow/tensor.py

@@ -210,3 +210,15 @@ def zeros_like(input_tensor):
 
 def matmul(x, y):
     return tf.linalg.matmul(x, y)
+
+
+def l1_regularization(l1):
+    return tf.keras.regularizers.L1(l1=l1)
+
+
+def l2_regularization(l2):
+    return tf.keras.regularizers.L2(l2=l2)
+
+
+def l1_l2_regularization(l1, l2):
+    return tf.keras.regularizers.L1L2(l1=l1, l2=l2)

deepxde/backend/tensorflow_compat_v1/tensor.py

@@ -245,3 +245,15 @@ def matmul(x, y):
 
 def sparse_dense_matmul(x, y):
     return tf.sparse.sparse_dense_matmul(x, y)
+
+
+def l1_regularization(l1):
+    return tf.keras.regularizers.L1(l1=l1)
+
+
+def l2_regularization(l2):
+    return tf.keras.regularizers.L2(l2=l2)
+
+
+def l1_l2_regularization(l1, l2):
+    return tf.keras.regularizers.L1L2(l1=l1, l2=l2)

deepxde/callbacks.py

@@ -137,7 +137,7 @@ def __init__(
         self.monitor = monitor
         self.monitor_op = np.less
         self.epochs_since_last_save = 0
-        self.best = np.Inf
+        self.best = np.inf
 
     def on_epoch_end(self):
         self.epochs_since_last_save += 1
@@ -221,7 +221,7 @@ def on_train_begin(self):
         if self.baseline is not None:
             self.best = self.baseline
         else:
-            self.best = np.Inf if self.monitor_op == np.less else -np.Inf
+            self.best = np.inf if self.monitor_op == np.less else -np.inf
 
     def on_epoch_end(self):
         if self.model.train_state.epoch < self.start_from_epoch:

deepxde/data/quadruple.py

@@ -94,7 +94,7 @@ def train_next_batch(self, batch_size=None):
             self.train_x[0][indices_branch],
             self.train_x[1][indices_branch],
             self.train_x[2][indices_trunk],
-        ), self.train_y[indices_branch, indices_trunk]
+        ), self.train_y[indices_branch][:, indices_trunk]
 
     def test(self):
         return self.test_x, self.test_y

deepxde/geometry/geometry_nd.py

@@ -81,6 +81,37 @@ def uniform_points(self, n, boundary=True):
             )
         return x
 
+    def uniform_boundary_points(self, n):
+        points_per_face = max(1, n // (2 * self.dim))
+        points = []
+        for d in range(self.dim):
+            for boundary in [self.xmin[d], self.xmax[d]]:
+                xi = []
+                for i in range(self.dim):
+                    if i == d:
+                        xi.append(np.array([boundary], dtype=config.real(np)))
+                    else:
+                        ni = int(np.ceil(points_per_face ** (1 / (self.dim - 1))))
+                        xi.append(
+                            np.linspace(
+                                self.xmin[i],
+                                self.xmax[i],
+                                num=ni + 1,
+                                endpoint=False,
+                                dtype=config.real(np),
+                            )[1:]
+                        )
+                face_points = np.array(list(itertools.product(*xi)))
+                points.append(face_points)
+        points = np.vstack(points)
+        if n != len(points):
+            print(
+                "Warning: {} points required, but {} points sampled.".format(
+                    n, len(points)
+                )
+            )
+        return points
+
     def random_points(self, n, random="pseudo"):
         x = sample(n, self.dim, random)
         return (self.xmax - self.xmin) * x + self.xmin

deepxde/gradients/gradients.py

@@ -67,3 +67,5 @@ def clear():
     """Clear cached Jacobians and Hessians."""
     if config.autodiff == "reverse":
         gradients_reverse.clear()
+    elif config.autodiff == "forward":
+        gradients_forward.clear()

deepxde/gradients/gradients_forward.py

@@ -110,3 +110,8 @@ def jacobian(ys, xs, i=None, j=None):
 def hessian(ys, xs, component=0, i=0, j=0):
     dys_xj = jacobian(ys, xs, i=None, j=j)
     return jacobian(dys_xj, xs, i=component, j=i)
+
+
+def clear():
+    """Clear cached Jacobians"""
+    jacobian._Jacobians.clear()